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Processing in-Memory AI Chips Market Set to Skyrocket from $231M in 2025 to $44B by 2032 at 112.4% CAGR | Valuates Reports

Published

6 months ago

on

What is the Market Size of Processing in-Memory AI Chips?

BANGALORE, India, April 29, 2026 /PRNewswire/ — The global Processing in-memory AI Chips market was valued at USD 231 Million in 2025 and is anticipated to reach USD 44335 Million by 2032, at a CAGR of 112.4% from 2026 to 2032.

 

 

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What are the key factors driving the growth of the Processing in-Memory AI Chips Market?

The processing in-memory AI chips market is expanding due to growing pressures on compute architectures from data movement inefficiency, latency constraints, rising power sensitivity, and deployment cost control across AI workloads.Demand is shifting toward chip designs that minimize the distance between memory and computation, enabling faster inference execution and better throughput under constrained thermal and energy conditions.This trend is especially relevant for workloads where bandwidth pressure, response time, and local processing efficiency directly determine system value.The market benefits from broader interest in architectures supporting both edge and data center AI tasks, without full reliance on conventional processor-memory separation.These factors create a strong commercial foundation for processing in-memory adoption.

Source from Valuates Reports: https://reports.valuates.com/market-reports/QYRE-Auto-15O17238/global-processing-in-memory-ai-chips

TRENDS INFLUENCING THE GROWTH OF THE PROCESSING IN-MEMORY AI CHIPS MARKET:

DRAM-PIM is driving growth in the processing in-memory AI chips market by addressing one of the most persistent bottlenecks in AI computing, which is the heavy cost of transferring data between memory and logic. By embedding compute capability closer to high-capacity memory structures, DRAM-PIM improves efficiency in bandwidth-intensive inference and parallel data handling environments. This makes it highly relevant for larger models and workloads that require sustained access to large datasets with lower latency overhead. Its role in improving throughput while reducing external data shuttling is strengthening its position in advanced AI infrastructure, particularly where performance scaling must happen without proportionate increases in power draw or board-level complexity.

SRAM-PIM is supporting market growth by serving AI use cases that prioritize low latency, fast local access, and power-efficient computation in compact environments. Its architectural suitability for tightly coupled memory and processing enables faster execution of inference tasks where response speed is critical and repeated memory access patterns are concentrated. This makes SRAM-PIM especially attractive in edge AI systems, embedded intelligence platforms, and applications where energy budgets and footprint limitations are decisive purchase factors. As device-side intelligence becomes more valuable across industrial, consumer, and autonomous systems, SRAM-PIM is gaining traction as a practical route to delivering on-chip efficiency without the penalties associated with conventional memory-transfer-heavy architectures.

In-memory processing chips are driving the growth of the processing in-memory AI chips market by creating a more application-aligned hardware approach for modern AI inference. Their appeal lies in improving usable performance per watt, reducing system bottlenecks, and enabling more scalable deployment economics across both small and large computing power environments. These chips are increasingly viewed as a structural response to the limitations of traditional architectures in handling AI workloads efficiently. As buyers seek solutions that can balance throughput, heat, latency, and integration flexibility, in-memory processing chips are moving from niche experimentation toward broader commercial adoption, supporting a market that is increasingly defined by workload efficiency rather than raw compute expansion alone.

A major factor supporting the market is the growing need to reduce the cost of data movement inside AI systems. In conventional architectures, moving data back and forth between memory and processors consumes time, power, and system resources. Processing in-memory chips directly address this problem by bringing computation closer to stored data. This improves execution efficiency and makes the architecture attractive for inference-heavy environments where repetitive data access creates performance drag. As buyers increasingly evaluate compute systems based on usable efficiency rather than nominal processing strength, demand for architectures that minimize data transport overhead continues to strengthen the market.

Power efficiency is emerging as a decisive growth factor for the processing in-memory AI chips market. AI deployment is no longer limited to environments where power availability is secondary. Enterprises, edge operators, and embedded system developers now require hardware that can support meaningful intelligence under tight energy and thermal budgets. Processing in-memory designs improve energy utilization by reducing unnecessary memory access traffic and enabling more efficient task execution. This gives them strong relevance in a market where lower operating cost, thermal manageability, and sustained performance matter as much as raw computational output, especially across continuously running inference systems and distributed AI infrastructure.

The expansion of edge AI is supporting market growth by increasing demand for chips that can perform inference closer to the source of data. Edge systems need fast decision-making, low energy consumption, and compact integration, all of which align well with processing in-memory designs. As intelligence moves into cameras, sensors, industrial devices, and smart endpoints, conventional architectures often face efficiency tradeoffs that reduce suitability in such environments. Processing in-memory chips help overcome these limitations by supporting local computation with lower latency and reduced data transfer dependency. This makes the technology increasingly relevant as edge intelligence shifts from optional capability to essential product differentiation.

The growing complexity of AI inference workloads is creating favorable conditions for processing in-memory adoption. As models become more memory-intensive and inference demand spreads across commercial applications, the limitations of traditional compute-memory separation become harder to ignore. Buyers are looking for architectures that can handle repeated memory access more efficiently and sustain performance under real deployment conditions. Processing in-memory chips respond to this need by improving memory interaction efficiency, which is particularly valuable in workloads where bandwidth and latency determine real-world usefulness. This shift is helping the market as hardware decisions become increasingly shaped by inference practicality rather than theoretical compute scale.

The market is also benefiting from a growing emphasis on cost-per-inference rather than simple peak performance comparisons. Buyers increasingly want AI hardware that can deliver consistent workload execution with better efficiency, lower supporting infrastructure requirements, and more practical deployment economics. Processing in-memory chips are well positioned in this context because they help reduce some of the overhead traditionally associated with memory bottlenecks, energy consumption, and system complexity. Their value proposition becomes stronger when purchasing decisions are based on long-term operating efficiency and scalable deployment. This cost discipline is pushing interest toward architectures that offer more balanced performance across real commercial use cases.

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What are the major product types in the Processing in-memory AI Chips Market?

DRAM-PIMSRAM-PIM

What are the main applications of the Processing in-memory AI Chips Market?

Near-Memory Computing (PNM) ChipIn-Memory Processing (PIM) ChipIn-Memory Computing (CIM) Chip

Key Players in the Processing in-memory AI Chips Market:

MyhticSyntiantD-MatrixHangzhou Zhicun (Witmem) TechnologyBeijing Pingxin TechnologyAistarTekSAMSUNGSK HynixShenzhen Reexen TechnologyGraphcoreAxelera AISuzhou Yizhu Intelligent TechnologyBeijing Houmo TechnologyEnCharge AI

Which region dominates the Processing in-memory AI chips market?

Asia-Pacific remains the most dynamic region due to its deep semiconductor ecosystem, expanding edge device manufacturing base, strong memory technology orientation, and increasing integration of AI into consumer and industrial electronics. China is supporting market formation through locally aligned compute architecture development, while South Korea, Japan, and Taiwan provide supply-side depth through memory and advanced chip ecosystem capabilities. Other regions are adopting more gradually, mainly through selective edge AI and infrastructure modernization use cases.

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What are some related markets to the Processing in-memory ai chips market?

Computing in Memory Technology Market was valued at USD 268 Million in the year 2024 and is projected to reach a revised size of USD 175260 Million by 2031, growing at a CAGR of 154.7% during the forecast period.In-memory Computing Chips for AI market was valued at USD 231 Million in 2025 and is anticipated to reach USD 44335 Million by 2032, at a CAGR of 112.4% from 2026 to 2032.HTAP-Enabling In-Memory Computing Technologies MarketIMDG (In-Memory Data Grid) Software Market Research ReportEmbedded Ai Chips Market Research ReportUltra-low Power AI Chips Market Research ReportHigh-Bandwidth Memory Chips Market was valued at USD 3816 Million in the year 2024 and is projected to reach a revised size of USD 139450 Million by 2031, growing at a CAGR of 68.2% during the forecast period.LPDDR Chips Market was valued at USD 6891 Million in the year 2024 and is projected to reach a revised size of USD 10870 Million by 2031, growing at a CAGR of 6.8% during the forecast period.Semiconductor Memory Market was valued at USD 125890 Million in the year 2024 and is projected to reach a revised size of USD 232900 Million by 2031, growing at a CAGR of 9.3% during the forecast period.AI Calculus Chips Market was valued at USD 46520 Million in the year 2024 and is projected to reach a revised size of USD 269300 Million by 2031, growing at a CAGR of 25.1% during the forecast period.Military Chips Market was valued at USD 1168 Million in the year 2024 and is projected to reach a revised size of USD 1583 Million by 2031, growing at a CAGR of 4.5% during the forecast period.

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Axon Announces Body 5: Next-Generation Camera Built for the AI Era

Published

2 hours ago

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October 24, 2026

By

Axon Body 5 delivers 25x the AI processing power of earlier-generation cameras, with upgraded connectivity, audio and battery life to support real-time situational awareness in the field

SCOTTSDALE, Ariz., Oct. 24, 2026 /PRNewswire/ — Axon (Nasdaq: AXON), the global leader in public safety technology, today announced Axon Body 5, its newest body-worn camera designed to bring more powerful intelligence, connectivity and real-time support directly to officers in the field.

For years, Axon body cameras have helped agencies capture critical evidence and create greater transparency. Body 5 is built to help officers during the call, with information, assistance and backup on the spot.

“Officers shouldn’t have to choose between getting information and watching the scene. With Axon Body 5, they don’t,” said Rick Smith, Axon Founder and CEO. “They capture a license or document with the camera, keep their hands free and eyes up, and get information faster than writing it down and calling it in by radio. An officer fully engaged with the person in front of them, not down at a notepad, is a safer officer and a better-served community.”

Axon Body 5: Built for the AI Era

Built from the ground up for the AI era, Axon Body 5 delivers 25x the AI processing power than earlier generation Axon body cameras, alongside advancements in connectivity, audio, video and battery life. Together, these upgrades give the camera the physical power to run smarter AI features — both today and as new AI tools are developed in the future — while also making it more dependable for officers to use every day. 

For the first time, Axon is also introducing two versions of its latest flagship camera: Axon Body 5 and Axon Body 5 Pro.

Both models feature several key advancements, including:

25x more processing power for AI: A powerful new computing architecture creates the capacity to support current and future AI features directly on the body camera.Enhanced video: Improved video captures clearer evidence across challenging environments, including better low-light performance.Improved connectivity: Seamless connectivity in urban and remote environments – and more precise location capability – helps officers, cameras and remote support teams stay connected when timely information matters most.Longer battery life: 20% more battery life than the previous generation helps keep the camera ready and connected throughout an officer’s shift.

The Axon Body 5 Pro will continue to gain new AI capabilities as they become available, growing along with the AI Era Plan. Additionally, new hardware features include:

A near-focus camera: An additional dedicated camera lens lets officers scan driver’s licenses to check status and search for wants or warrants in seconds, without returning to the patrol vehicle, with relevant information sent to Axon Fusus to support real-time operations.Front-facing speakers: Four additional speakers project even more powerful audio toward the person an officer is speaking with, helping real-time translations from Axon Assistant carry more clearly over heavy traffic or crowd noise. Additional memory: Extra space lets the camera run several AI tools at once, helping officers stay better informed and work faster.

Bringing More of Axon’s AI Era Into the Field

Axon Body 5 helps officers get more out of the Axon AI Era Plan by bringing smart tools right to the scene. Axon’s portfolio already supports public safety professionals across a growing range of tasks. For example, Draft One helps officers turn body-camera audio into high-quality report drafts and Axon Assistant provides voice-based information access and real-time translation.

Increasingly, Axon is also bringing AI into real-time operations.

Axon Guardian is a new category of AI features within the AI Era Plan that was specifically created to work with an officer’s field-based devices – starting with body cameras and in-car cameras – to improve safety. Guardian is designed to flag potential risks as situations unfold and create alerts when additional attention may be needed. Two of the first features include:

Escalation Detection: When an Axon Body 4 or Axon Body 5 camera is activated and recording, Guardian has the ability to detect potential conflicts in real time and create an alert in Axon Fusus. A human reviews the livestream and decides if additional support or action is needed.Approach Alert: When active on Axon Fleet 3, Guardian can detect if the door of a stopped vehicle opens and alert the officer through the in-car display and their body-worn camera.

Combined, Axon Body 5 and the expanding AI Era portfolio demonstrate the importance of AI as a critical resource to assist public safety professionals before, during and after an incident — reducing administrative burden, improving situational awareness and helping protect more lives.

Innovating Responsibly

Axon’s Responsible Innovation framework guides the development of all of its products. Designed with this in mind, Axon Body 5 and Guardian prioritize human oversight and maintain agency control over data. As a key part of the responsible innovation process, Axon’s independent Ethics and Equity Advisory Council was created to help responsibly develop and deploy new technologies with transparency, while aligning with community expectations.

General availability dates for Axon Body 5 and Axon Guardian will be announced soon. Product features and capabilities vary by configuration. For more information on Axon’s current products and services: Axon Product Catalog – Axon.com.

About Axon
Axon (Nasdaq: AXON) is the global leader in public safety technology supporting state, local, federal and international public safety agencies with technology designed to Protect Life. Complementing its mission, Axon’s growing Enterprise market focuses on protecting critical infrastructure and frontline workers across multiple industries including corporate security, hospitality, retail and healthcare. Founder-led since 1993, Axon builds products with transparency and responsibility. From TASER energy devices to today’s connected network of hardware – including cameras, drones, and robotics – cloud software, sensors, and AI-tools, the Axon ecosystem helps seamlessly protect people and places when it matters most. To ensure these technologies align with community expectations, Axon’s independent Ethics & Equity Advisory Council guides the ethical, inclusive, and accountable design of its products and services. Learn more about the EEAC and Axon’s Responsible Innovation framework at Axon Responsibility. 

Non-Axon trademarks are the property of their respective owners. Axon, TASER and the Delta Logo are trademarks of Axon Enterprise, Inc., some of which are registered in the US and other countries. For more information, visit www.axon.com/legal. All rights reserved.

Please visit https://investor.axon.com and https://www.axon.com/newsroom for more information.

Media Contact:
Kate MacKinnon
Vice President, Communications
press@axon.com

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In HelloNation, Custom Fabrication Expert Mark Coyle Explains a Common Mistake in Custom Aluminum Design

Published

3 hours ago

on

October 24, 2026

By

The article explains why smart geometry, not thicker walls, creates stronger, more efficient aluminum components.

ROCHESTER, N.Y., Oct. 24, 2026 /PRNewswire/ — What early design choice causes many custom aluminum projects to become heavier, costlier, or harder to manufacture? That question is addressed in a HelloNation article featuring insights from Custom Fabrication Expert Mark Coyle of American Custom Metals, Inc. in Rochester, New York, that outlines how misunderstanding aluminum behavior leads designers to rely on thick walls instead of stronger geometry.

The article begins by explaining that many people assume thicker walls automatically make aluminum parts stronger. It notes that aluminum does not respond to wall thickness the way steel does. Instead, its performance is shaped by how the metal flows through the extrusion die. The article states that custom aluminum design is more effective when profiles use ribs, chambers, and flanges to create stiffness without adding unnecessary weight. This guidance helps readers understand why adding material often increases cost without improving strength.

The article also highlights that thicker walls change how aluminum moves during extrusion. When heavy areas fill more slowly, the metal cools unevenly and can develop stress that only appears later. The HelloNation piece explains that well-balanced geometry keeps extrusion smooth and predictable. This consistency makes it easier to maintain dimensional accuracy across long production runs. By focusing on geometry, custom aluminum design avoids the problems that come with slow extrusion speeds and uneven cooling.

Another insight in the article centers on how ribs and internal supports influence stability. It describes how a thin wall supported by a properly placed rib often performs better than a thick wall with no reinforcement. This happens because the rib helps distribute load, improve stiffness, and guide the aluminum as it cools. The article explains that this structural approach makes custom aluminum design more efficient and gives designers more control over how the final part behaves under pressure.

The article also shows why early collaboration between designer and fabricator prevents costly changes. It states that a fabricator familiar with extrusion can catch potential issues before a die is built. Small adjustments to corners or transitions can remove weak points or improve metal flow. The article notes that this teamwork keeps the design aligned with real-world production. By working together early, both sides reduce the risk of redesigning the die after the first test run.

Machining is another area where the article explains how wall thickness affects performance. Features such as deep pockets or sharp internal angles may require slow feed rates or special tooling. When walls are thicker than needed, those areas can become difficult to machine cleanly. The article states that thoughtful custom aluminum design makes machining more predictable. Small changes, such as adding radiused edges or sizing features to match standard tools, help shorten cycle times and prevent tool wear.

Thermal behavior receives detailed attention as well. The article explains that aluminum retains heat differently depending on the mass of each section. Thicker walls hold heat longer, which increases the chance of distortion during extrusion, machining, or finishing. When a design uses geometry to create strength, the material cools more evenly. This reduces warping and helps long profiles stay aligned. The article notes that stable cooling is important for components that require a precise fit and consistent shape throughout an entire production run.

Cost is also a key topic in the article. It states that heavier profiles increase material use, extrusion time, and tooling needs. A custom aluminum design that relies on geometry instead of mass lowers material consumption and keeps the fabrication process efficient. The article explains that lighter parts are easier to handle, ship, and assemble. This saves money across multiple stages of production and supports long-term manufacturing efficiency.

The article also notes that smart custom aluminum design creates profiles that behave predictably during extrusion and machining. Smooth transitions, balanced wall dimensions, and structural features help reduce the chance of distortion or dimensional drift. These elements also lower scrap rates and support reliable performance. By using geometry instead of excess material, designers build parts that maintain strength without compromising manufacturability.

As the article concludes, it emphasizes that avoiding thick walls is one of the best ways to improve performance and lower cost. It states that the most dependable custom aluminum components come from designs that use structural features to strengthen the profile. When designers understand how aluminum behaves at each stage of extrusion and machining, they create parts that are strong, efficient, and consistent from batch to batch. This approach turns custom aluminum design into a predictable process rather than a series of uncertain adjustments.

A Common Mistake in Custom Aluminum Design features insights from Mark Coyle, Custom Fabrication Expert of Rochester, NY, in HelloNation.

About HelloNation
HelloNation is America’s Good News Network, a premier media platform built on the idea that good news travels faster when real people tell real stories. Through its community-focused publications and innovative “edvertising” approach, HelloNation delivers content that informs, inspires, and spotlights the leaders making a meaningful impact in their communities.

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World Young Scientist Summit 2026 Opens in Wenzhou, Zhejiang

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4 hours ago

on

October 24, 2026

By

WENZHOU, China, Oct. 24, 2026 /PRNewswire/ — This is a report by China Daily:

On October 24, the World Young Scientist Summit 2026 opened in Wenzhou, Zhejiang. The event is hosted by the China Association for Science and Technology (CAST), the Zhejiang Provincial People’s Government, and the World Association of Young Scientists. Nearly 800 representatives, including scientists, educators, and entrepreneurs from more than 60 countries and regions, are attending the summit.

He Junke, Vice President of CAST in charge of daily work and First Secretary of its Secretariat; Lian Yimin, Chairman and Party Group Secretary of the Zhejiang Provincial Committee of the Chinese People’s Political Consultative Conference; Marcelo Knobel, Executive Director of The World Academy of Sciences; and Zhang Zhenfeng, Member of the Standing Committee of the CPC Zhejiang Provincial Committee and Secretary of the CPC Wenzhou Municipal Committee, attended the summit and delivered remarks. Yao Gaoyuan, Vice Governor of the Zhejiang Provincial People’s Government, presided over the first segment of the plenary session.

With the theme “Gathering Talents Worldwide to Create a Better Future,” the summit’s annual agenda is “Global Youth Action in the AI Era: Building a Sustainable Future.” It aims to draw on the insights of young people around the world, deepen international sci-tech cooperation, and jointly address questions about young people, science, the world and the future.

Efim Zelmanov, Fields Medalist and Foreign Member of the Chinese Academy of Sciences; Chen Yunji, Deputy Director of the Institute of Computing Technology, CAS, and Director of the National Key Laboratory of Processor Chips; Mary Garson, President of the International Union of Pure and Applied Chemistry; and Lu Chaoyang, Member of the Standing Committee of CAST, Executive Dean of the Shanghai Institute of Advanced Study, University of Science and Technology of China, and Chairman of the Council of the World Association of Young Scientists, delivered keynote speeches respectively on education reform in the AI era, AI and processor chips, young women scientists’ participation in science, and quantum computing. They explored new opportunities for scientific research, talent cultivation, and international cooperation in the AI era.

This year’s summit placed greater emphasis on the growth needs of young scientists, with young talents under the age of 45 accounting for 88 percent of participants. It aimed to build a cluster of international journals, bring together young scientific talent, facilitate a batch of international collaborative projects and advance the application and commercialization of scientific and technological achievements. The summit released the Report on the Distribution and Mobility of Young Scientists in the Field of Embodied Intelligence, launched the World Young Scientist Summit Partner Journal Program and the 2026 World Young Scientist Innovation and Entrepreneurship Growth Support Program. It also published the “Young Scientist Pioneers 2035” list and announced the 2026 Sustainable Development Young Scientist Award. The awards were presented to four Chinese and foreign scientists.

Chen Tianrun, Founder of Hangzhou Moxin Technology Co., Ltd.; Yuan Yang, Assistant to the Dean of the Institute for Interdisciplinary Information Sciences at Tsinghua University; and two of the 2026 Sustainable Development Young Scientist Award winners, César de la Fuente and Nicola Segata, among other Chinese and foreign young scientist representatives, spoke at the flash-talk session to share their research practices and reflections.

In addition, the conference focused on fields such as future manufacturing, quantum information, embodied intelligence, brain-computer intelligence, environmental science, deep-sea resources, and life and health, and held 12 parallel forums. It also included the “Gravitation π” Future Technology and Concept Products Exhibition and Exchange, local thematic side events, and sci-tech and humanities study visits, further building an interdisciplinary, cross-regional and cross-sector platform for youth sci-tech exchange and cooperation.

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SOURCE China Daily

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Processing in-Memory AI Chips Market Set to Skyrocket from $231M in 2025 to $44B by 2032 at 112.4% CAGR | Valuates Reports

Published

6 months ago

on

What is the Market Size of Processing in-Memory AI Chips?

BANGALORE, India, April 29, 2026 /PRNewswire/ — The global Processing in-memory AI Chips market was valued at USD 231 Million in 2025 and is anticipated to reach USD 44335 Million by 2032, at a CAGR of 112.4% from 2026 to 2032.

 

 

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What are the key factors driving the growth of the Processing in-Memory AI Chips Market?

The processing in-memory AI chips market is expanding due to growing pressures on compute architectures from data movement inefficiency, latency constraints, rising power sensitivity, and deployment cost control across AI workloads.Demand is shifting toward chip designs that minimize the distance between memory and computation, enabling faster inference execution and better throughput under constrained thermal and energy conditions.This trend is especially relevant for workloads where bandwidth pressure, response time, and local processing efficiency directly determine system value.The market benefits from broader interest in architectures supporting both edge and data center AI tasks, without full reliance on conventional processor-memory separation.These factors create a strong commercial foundation for processing in-memory adoption.

Source from Valuates Reports: https://reports.valuates.com/market-reports/QYRE-Auto-15O17238/global-processing-in-memory-ai-chips

TRENDS INFLUENCING THE GROWTH OF THE PROCESSING IN-MEMORY AI CHIPS MARKET:

DRAM-PIM is driving growth in the processing in-memory AI chips market by addressing one of the most persistent bottlenecks in AI computing, which is the heavy cost of transferring data between memory and logic. By embedding compute capability closer to high-capacity memory structures, DRAM-PIM improves efficiency in bandwidth-intensive inference and parallel data handling environments. This makes it highly relevant for larger models and workloads that require sustained access to large datasets with lower latency overhead. Its role in improving throughput while reducing external data shuttling is strengthening its position in advanced AI infrastructure, particularly where performance scaling must happen without proportionate increases in power draw or board-level complexity.

SRAM-PIM is supporting market growth by serving AI use cases that prioritize low latency, fast local access, and power-efficient computation in compact environments. Its architectural suitability for tightly coupled memory and processing enables faster execution of inference tasks where response speed is critical and repeated memory access patterns are concentrated. This makes SRAM-PIM especially attractive in edge AI systems, embedded intelligence platforms, and applications where energy budgets and footprint limitations are decisive purchase factors. As device-side intelligence becomes more valuable across industrial, consumer, and autonomous systems, SRAM-PIM is gaining traction as a practical route to delivering on-chip efficiency without the penalties associated with conventional memory-transfer-heavy architectures.

In-memory processing chips are driving the growth of the processing in-memory AI chips market by creating a more application-aligned hardware approach for modern AI inference. Their appeal lies in improving usable performance per watt, reducing system bottlenecks, and enabling more scalable deployment economics across both small and large computing power environments. These chips are increasingly viewed as a structural response to the limitations of traditional architectures in handling AI workloads efficiently. As buyers seek solutions that can balance throughput, heat, latency, and integration flexibility, in-memory processing chips are moving from niche experimentation toward broader commercial adoption, supporting a market that is increasingly defined by workload efficiency rather than raw compute expansion alone.

A major factor supporting the market is the growing need to reduce the cost of data movement inside AI systems. In conventional architectures, moving data back and forth between memory and processors consumes time, power, and system resources. Processing in-memory chips directly address this problem by bringing computation closer to stored data. This improves execution efficiency and makes the architecture attractive for inference-heavy environments where repetitive data access creates performance drag. As buyers increasingly evaluate compute systems based on usable efficiency rather than nominal processing strength, demand for architectures that minimize data transport overhead continues to strengthen the market.

Power efficiency is emerging as a decisive growth factor for the processing in-memory AI chips market. AI deployment is no longer limited to environments where power availability is secondary. Enterprises, edge operators, and embedded system developers now require hardware that can support meaningful intelligence under tight energy and thermal budgets. Processing in-memory designs improve energy utilization by reducing unnecessary memory access traffic and enabling more efficient task execution. This gives them strong relevance in a market where lower operating cost, thermal manageability, and sustained performance matter as much as raw computational output, especially across continuously running inference systems and distributed AI infrastructure.

The expansion of edge AI is supporting market growth by increasing demand for chips that can perform inference closer to the source of data. Edge systems need fast decision-making, low energy consumption, and compact integration, all of which align well with processing in-memory designs. As intelligence moves into cameras, sensors, industrial devices, and smart endpoints, conventional architectures often face efficiency tradeoffs that reduce suitability in such environments. Processing in-memory chips help overcome these limitations by supporting local computation with lower latency and reduced data transfer dependency. This makes the technology increasingly relevant as edge intelligence shifts from optional capability to essential product differentiation.

The growing complexity of AI inference workloads is creating favorable conditions for processing in-memory adoption. As models become more memory-intensive and inference demand spreads across commercial applications, the limitations of traditional compute-memory separation become harder to ignore. Buyers are looking for architectures that can handle repeated memory access more efficiently and sustain performance under real deployment conditions. Processing in-memory chips respond to this need by improving memory interaction efficiency, which is particularly valuable in workloads where bandwidth and latency determine real-world usefulness. This shift is helping the market as hardware decisions become increasingly shaped by inference practicality rather than theoretical compute scale.

The market is also benefiting from a growing emphasis on cost-per-inference rather than simple peak performance comparisons. Buyers increasingly want AI hardware that can deliver consistent workload execution with better efficiency, lower supporting infrastructure requirements, and more practical deployment economics. Processing in-memory chips are well positioned in this context because they help reduce some of the overhead traditionally associated with memory bottlenecks, energy consumption, and system complexity. Their value proposition becomes stronger when purchasing decisions are based on long-term operating efficiency and scalable deployment. This cost discipline is pushing interest toward architectures that offer more balanced performance across real commercial use cases.

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What are the major product types in the Processing in-memory AI Chips Market?

DRAM-PIMSRAM-PIM

What are the main applications of the Processing in-memory AI Chips Market?

Near-Memory Computing (PNM) ChipIn-Memory Processing (PIM) ChipIn-Memory Computing (CIM) Chip

Key Players in the Processing in-memory AI Chips Market:

MyhticSyntiantD-MatrixHangzhou Zhicun (Witmem) TechnologyBeijing Pingxin TechnologyAistarTekSAMSUNGSK HynixShenzhen Reexen TechnologyGraphcoreAxelera AISuzhou Yizhu Intelligent TechnologyBeijing Houmo TechnologyEnCharge AI

Which region dominates the Processing in-memory AI chips market?

Asia-Pacific remains the most dynamic region due to its deep semiconductor ecosystem, expanding edge device manufacturing base, strong memory technology orientation, and increasing integration of AI into consumer and industrial electronics. China is supporting market formation through locally aligned compute architecture development, while South Korea, Japan, and Taiwan provide supply-side depth through memory and advanced chip ecosystem capabilities. Other regions are adopting more gradually, mainly through selective edge AI and infrastructure modernization use cases.

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What are some related markets to the Processing in-memory ai chips market?

Computing in Memory Technology Market was valued at USD 268 Million in the year 2024 and is projected to reach a revised size of USD 175260 Million by 2031, growing at a CAGR of 154.7% during the forecast period.In-memory Computing Chips for AI market was valued at USD 231 Million in 2025 and is anticipated to reach USD 44335 Million by 2032, at a CAGR of 112.4% from 2026 to 2032.HTAP-Enabling In-Memory Computing Technologies MarketIMDG (In-Memory Data Grid) Software Market Research ReportEmbedded Ai Chips Market Research ReportUltra-low Power AI Chips Market Research ReportHigh-Bandwidth Memory Chips Market was valued at USD 3816 Million in the year 2024 and is projected to reach a revised size of USD 139450 Million by 2031, growing at a CAGR of 68.2% during the forecast period.LPDDR Chips Market was valued at USD 6891 Million in the year 2024 and is projected to reach a revised size of USD 10870 Million by 2031, growing at a CAGR of 6.8% during the forecast period.Semiconductor Memory Market was valued at USD 125890 Million in the year 2024 and is projected to reach a revised size of USD 232900 Million by 2031, growing at a CAGR of 9.3% during the forecast period.AI Calculus Chips Market was valued at USD 46520 Million in the year 2024 and is projected to reach a revised size of USD 269300 Million by 2031, growing at a CAGR of 25.1% during the forecast period.Military Chips Market was valued at USD 1168 Million in the year 2024 and is projected to reach a revised size of USD 1583 Million by 2031, growing at a CAGR of 4.5% during the forecast period.

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Axon Announces Body 5: Next-Generation Camera Built for the AI Era

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2 hours ago

on

October 24, 2026

By

Axon Body 5 delivers 25x the AI processing power of earlier-generation cameras, with upgraded connectivity, audio and battery life to support real-time situational awareness in the field

SCOTTSDALE, Ariz., Oct. 24, 2026 /PRNewswire/ — Axon (Nasdaq: AXON), the global leader in public safety technology, today announced Axon Body 5, its newest body-worn camera designed to bring more powerful intelligence, connectivity and real-time support directly to officers in the field.

For years, Axon body cameras have helped agencies capture critical evidence and create greater transparency. Body 5 is built to help officers during the call, with information, assistance and backup on the spot.

“Officers shouldn’t have to choose between getting information and watching the scene. With Axon Body 5, they don’t,” said Rick Smith, Axon Founder and CEO. “They capture a license or document with the camera, keep their hands free and eyes up, and get information faster than writing it down and calling it in by radio. An officer fully engaged with the person in front of them, not down at a notepad, is a safer officer and a better-served community.”

Axon Body 5: Built for the AI Era

Built from the ground up for the AI era, Axon Body 5 delivers 25x the AI processing power than earlier generation Axon body cameras, alongside advancements in connectivity, audio, video and battery life. Together, these upgrades give the camera the physical power to run smarter AI features — both today and as new AI tools are developed in the future — while also making it more dependable for officers to use every day. 

For the first time, Axon is also introducing two versions of its latest flagship camera: Axon Body 5 and Axon Body 5 Pro.

Both models feature several key advancements, including:

25x more processing power for AI: A powerful new computing architecture creates the capacity to support current and future AI features directly on the body camera.Enhanced video: Improved video captures clearer evidence across challenging environments, including better low-light performance.Improved connectivity: Seamless connectivity in urban and remote environments – and more precise location capability – helps officers, cameras and remote support teams stay connected when timely information matters most.Longer battery life: 20% more battery life than the previous generation helps keep the camera ready and connected throughout an officer’s shift.

The Axon Body 5 Pro will continue to gain new AI capabilities as they become available, growing along with the AI Era Plan. Additionally, new hardware features include:

A near-focus camera: An additional dedicated camera lens lets officers scan driver’s licenses to check status and search for wants or warrants in seconds, without returning to the patrol vehicle, with relevant information sent to Axon Fusus to support real-time operations.Front-facing speakers: Four additional speakers project even more powerful audio toward the person an officer is speaking with, helping real-time translations from Axon Assistant carry more clearly over heavy traffic or crowd noise. Additional memory: Extra space lets the camera run several AI tools at once, helping officers stay better informed and work faster.

Bringing More of Axon’s AI Era Into the Field

Axon Body 5 helps officers get more out of the Axon AI Era Plan by bringing smart tools right to the scene. Axon’s portfolio already supports public safety professionals across a growing range of tasks. For example, Draft One helps officers turn body-camera audio into high-quality report drafts and Axon Assistant provides voice-based information access and real-time translation.

Increasingly, Axon is also bringing AI into real-time operations.

Axon Guardian is a new category of AI features within the AI Era Plan that was specifically created to work with an officer’s field-based devices – starting with body cameras and in-car cameras – to improve safety. Guardian is designed to flag potential risks as situations unfold and create alerts when additional attention may be needed. Two of the first features include:

Escalation Detection: When an Axon Body 4 or Axon Body 5 camera is activated and recording, Guardian has the ability to detect potential conflicts in real time and create an alert in Axon Fusus. A human reviews the livestream and decides if additional support or action is needed.Approach Alert: When active on Axon Fleet 3, Guardian can detect if the door of a stopped vehicle opens and alert the officer through the in-car display and their body-worn camera.

Combined, Axon Body 5 and the expanding AI Era portfolio demonstrate the importance of AI as a critical resource to assist public safety professionals before, during and after an incident — reducing administrative burden, improving situational awareness and helping protect more lives.

Innovating Responsibly

Axon’s Responsible Innovation framework guides the development of all of its products. Designed with this in mind, Axon Body 5 and Guardian prioritize human oversight and maintain agency control over data. As a key part of the responsible innovation process, Axon’s independent Ethics and Equity Advisory Council was created to help responsibly develop and deploy new technologies with transparency, while aligning with community expectations.

General availability dates for Axon Body 5 and Axon Guardian will be announced soon. Product features and capabilities vary by configuration. For more information on Axon’s current products and services: Axon Product Catalog – Axon.com.

About Axon
Axon (Nasdaq: AXON) is the global leader in public safety technology supporting state, local, federal and international public safety agencies with technology designed to Protect Life. Complementing its mission, Axon’s growing Enterprise market focuses on protecting critical infrastructure and frontline workers across multiple industries including corporate security, hospitality, retail and healthcare. Founder-led since 1993, Axon builds products with transparency and responsibility. From TASER energy devices to today’s connected network of hardware – including cameras, drones, and robotics – cloud software, sensors, and AI-tools, the Axon ecosystem helps seamlessly protect people and places when it matters most. To ensure these technologies align with community expectations, Axon’s independent Ethics & Equity Advisory Council guides the ethical, inclusive, and accountable design of its products and services. Learn more about the EEAC and Axon’s Responsible Innovation framework at Axon Responsibility. 

Non-Axon trademarks are the property of their respective owners. Axon, TASER and the Delta Logo are trademarks of Axon Enterprise, Inc., some of which are registered in the US and other countries. For more information, visit www.axon.com/legal. All rights reserved.

Please visit https://investor.axon.com and https://www.axon.com/newsroom for more information.

Media Contact:
Kate MacKinnon
Vice President, Communications
press@axon.com

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In HelloNation, Custom Fabrication Expert Mark Coyle Explains a Common Mistake in Custom Aluminum Design

Published

3 hours ago

on

October 24, 2026

By

The article explains why smart geometry, not thicker walls, creates stronger, more efficient aluminum components.

ROCHESTER, N.Y., Oct. 24, 2026 /PRNewswire/ — What early design choice causes many custom aluminum projects to become heavier, costlier, or harder to manufacture? That question is addressed in a HelloNation article featuring insights from Custom Fabrication Expert Mark Coyle of American Custom Metals, Inc. in Rochester, New York, that outlines how misunderstanding aluminum behavior leads designers to rely on thick walls instead of stronger geometry.

The article begins by explaining that many people assume thicker walls automatically make aluminum parts stronger. It notes that aluminum does not respond to wall thickness the way steel does. Instead, its performance is shaped by how the metal flows through the extrusion die. The article states that custom aluminum design is more effective when profiles use ribs, chambers, and flanges to create stiffness without adding unnecessary weight. This guidance helps readers understand why adding material often increases cost without improving strength.

The article also highlights that thicker walls change how aluminum moves during extrusion. When heavy areas fill more slowly, the metal cools unevenly and can develop stress that only appears later. The HelloNation piece explains that well-balanced geometry keeps extrusion smooth and predictable. This consistency makes it easier to maintain dimensional accuracy across long production runs. By focusing on geometry, custom aluminum design avoids the problems that come with slow extrusion speeds and uneven cooling.

Another insight in the article centers on how ribs and internal supports influence stability. It describes how a thin wall supported by a properly placed rib often performs better than a thick wall with no reinforcement. This happens because the rib helps distribute load, improve stiffness, and guide the aluminum as it cools. The article explains that this structural approach makes custom aluminum design more efficient and gives designers more control over how the final part behaves under pressure.

The article also shows why early collaboration between designer and fabricator prevents costly changes. It states that a fabricator familiar with extrusion can catch potential issues before a die is built. Small adjustments to corners or transitions can remove weak points or improve metal flow. The article notes that this teamwork keeps the design aligned with real-world production. By working together early, both sides reduce the risk of redesigning the die after the first test run.

Machining is another area where the article explains how wall thickness affects performance. Features such as deep pockets or sharp internal angles may require slow feed rates or special tooling. When walls are thicker than needed, those areas can become difficult to machine cleanly. The article states that thoughtful custom aluminum design makes machining more predictable. Small changes, such as adding radiused edges or sizing features to match standard tools, help shorten cycle times and prevent tool wear.

Thermal behavior receives detailed attention as well. The article explains that aluminum retains heat differently depending on the mass of each section. Thicker walls hold heat longer, which increases the chance of distortion during extrusion, machining, or finishing. When a design uses geometry to create strength, the material cools more evenly. This reduces warping and helps long profiles stay aligned. The article notes that stable cooling is important for components that require a precise fit and consistent shape throughout an entire production run.

Cost is also a key topic in the article. It states that heavier profiles increase material use, extrusion time, and tooling needs. A custom aluminum design that relies on geometry instead of mass lowers material consumption and keeps the fabrication process efficient. The article explains that lighter parts are easier to handle, ship, and assemble. This saves money across multiple stages of production and supports long-term manufacturing efficiency.

The article also notes that smart custom aluminum design creates profiles that behave predictably during extrusion and machining. Smooth transitions, balanced wall dimensions, and structural features help reduce the chance of distortion or dimensional drift. These elements also lower scrap rates and support reliable performance. By using geometry instead of excess material, designers build parts that maintain strength without compromising manufacturability.

As the article concludes, it emphasizes that avoiding thick walls is one of the best ways to improve performance and lower cost. It states that the most dependable custom aluminum components come from designs that use structural features to strengthen the profile. When designers understand how aluminum behaves at each stage of extrusion and machining, they create parts that are strong, efficient, and consistent from batch to batch. This approach turns custom aluminum design into a predictable process rather than a series of uncertain adjustments.

A Common Mistake in Custom Aluminum Design features insights from Mark Coyle, Custom Fabrication Expert of Rochester, NY, in HelloNation.

About HelloNation
HelloNation is America’s Good News Network, a premier media platform built on the idea that good news travels faster when real people tell real stories. Through its community-focused publications and innovative “edvertising” approach, HelloNation delivers content that informs, inspires, and spotlights the leaders making a meaningful impact in their communities.

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World Young Scientist Summit 2026 Opens in Wenzhou, Zhejiang

Published

4 hours ago

on

October 24, 2026

By

WENZHOU, China, Oct. 24, 2026 /PRNewswire/ — This is a report by China Daily:

On October 24, the World Young Scientist Summit 2026 opened in Wenzhou, Zhejiang. The event is hosted by the China Association for Science and Technology (CAST), the Zhejiang Provincial People’s Government, and the World Association of Young Scientists. Nearly 800 representatives, including scientists, educators, and entrepreneurs from more than 60 countries and regions, are attending the summit.

He Junke, Vice President of CAST in charge of daily work and First Secretary of its Secretariat; Lian Yimin, Chairman and Party Group Secretary of the Zhejiang Provincial Committee of the Chinese People’s Political Consultative Conference; Marcelo Knobel, Executive Director of The World Academy of Sciences; and Zhang Zhenfeng, Member of the Standing Committee of the CPC Zhejiang Provincial Committee and Secretary of the CPC Wenzhou Municipal Committee, attended the summit and delivered remarks. Yao Gaoyuan, Vice Governor of the Zhejiang Provincial People’s Government, presided over the first segment of the plenary session.

With the theme “Gathering Talents Worldwide to Create a Better Future,” the summit’s annual agenda is “Global Youth Action in the AI Era: Building a Sustainable Future.” It aims to draw on the insights of young people around the world, deepen international sci-tech cooperation, and jointly address questions about young people, science, the world and the future.

Efim Zelmanov, Fields Medalist and Foreign Member of the Chinese Academy of Sciences; Chen Yunji, Deputy Director of the Institute of Computing Technology, CAS, and Director of the National Key Laboratory of Processor Chips; Mary Garson, President of the International Union of Pure and Applied Chemistry; and Lu Chaoyang, Member of the Standing Committee of CAST, Executive Dean of the Shanghai Institute of Advanced Study, University of Science and Technology of China, and Chairman of the Council of the World Association of Young Scientists, delivered keynote speeches respectively on education reform in the AI era, AI and processor chips, young women scientists’ participation in science, and quantum computing. They explored new opportunities for scientific research, talent cultivation, and international cooperation in the AI era.

This year’s summit placed greater emphasis on the growth needs of young scientists, with young talents under the age of 45 accounting for 88 percent of participants. It aimed to build a cluster of international journals, bring together young scientific talent, facilitate a batch of international collaborative projects and advance the application and commercialization of scientific and technological achievements. The summit released the Report on the Distribution and Mobility of Young Scientists in the Field of Embodied Intelligence, launched the World Young Scientist Summit Partner Journal Program and the 2026 World Young Scientist Innovation and Entrepreneurship Growth Support Program. It also published the “Young Scientist Pioneers 2035” list and announced the 2026 Sustainable Development Young Scientist Award. The awards were presented to four Chinese and foreign scientists.

Chen Tianrun, Founder of Hangzhou Moxin Technology Co., Ltd.; Yuan Yang, Assistant to the Dean of the Institute for Interdisciplinary Information Sciences at Tsinghua University; and two of the 2026 Sustainable Development Young Scientist Award winners, César de la Fuente and Nicola Segata, among other Chinese and foreign young scientist representatives, spoke at the flash-talk session to share their research practices and reflections.

In addition, the conference focused on fields such as future manufacturing, quantum information, embodied intelligence, brain-computer intelligence, environmental science, deep-sea resources, and life and health, and held 12 parallel forums. It also included the “Gravitation π” Future Technology and Concept Products Exhibition and Exchange, local thematic side events, and sci-tech and humanities study visits, further building an interdisciplinary, cross-regional and cross-sector platform for youth sci-tech exchange and cooperation.

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SOURCE China Daily

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