Processing-in-Memory AI Chips Market Research 2026-2032: Market Size Forecast, Competitive Market Share Analysis, and Memory-Integration Segmentation for Von Neumann Bottleneck Mitigation

Global Leading Market Research Publisher QYResearch announces the release of its latest report “Processing in-memory AI Chips – Global Market Share and Ranking, Overall Sales and Demand Forecast 2026-2032”. Based on current situation and impact historical analysis (2021-2025) and forecast calculations (2026-2032), this report provides a comprehensive analysis of the global Processing in-memory AI Chips market, including market size, share, demand, industry development status, and forecasts for the next few years.

The global market for Processing in-memory AI Chips was estimated to be worth US231millionin2025andisprojectedtoreachUS231millionin2025andisprojectedtoreachUS 44,335 million, growing at a CAGR of 112.4% from 2026 to 2032.

Processing-in-Memory AI chips are computing architectures that integrate computation capabilities directly within or very close to memory arrays, enabling arithmetic operations—such as multiply-accumulate—to be performed where data is stored, thereby minimizing data movement between memory and processors; by alleviating the von Neumann bottleneck, PIM chips can significantly improve energy efficiency, bandwidth utilization, and latency, making them particularly suitable for AI workloads dominated by matrix and vector operations, while challenges remain in precision control, manufacturing variability, programmability, and ecosystem maturity as the technology transitions from research prototypes toward specialized commercial deployments.

AI system designers and data center operators face a fundamental and worsening challenge: the von Neumann bottleneck, where data movement between processor and memory consumes 80-90% of energy and dominates execution time in large-scale AI models. For a typical transformer inference (GPT-4 class, 1.8 trillion parameters), data movement accounts for 85% of total energy and 70% of latency, even with high-bandwidth memory (HBM) and advanced packaging. As model sizes double every 6-12 months (scaling laws), traditional GPU/ASIC accelerators face diminishing returns from architectural improvements alone. Processing-in-memory (PIM) AI chips address this bottleneck by integrating compute units directly into memory arrays (DRAM or SRAM), performing matrix-vector multiplication (the core of neural network inference and training) where data resides. PIM achieves 10-100x improvement in energy efficiency (10-100 TOPS/W vs. 1-10 TOPS/W for conventional accelerators) and 5-20x reduction in latency for memory-bound operations. This report delivers data-driven insights into market size, memory-type segmentation, computing power classification, and technology maturation across the 2026-2032 forecast period.

【Get a free sample PDF of this report (Including Full TOC, List of Tables & Figures, Chart)】
https://www.qyresearch.com/reports/5542524/processing-in-memory-ai-chips

1. Core Keywords and Market Definition: Von Neumann Bottleneck, Compute-in-Memory (CIM), and Multiply-Accumulate (MAC) Throughput

This analysis embeds three core keywords—Von Neumann Bottleneck, Compute-in-Memory (CIM) , and Multiply-Accumulate (MAC) Throughput—throughout the industry narrative. These terms define the architectural problem and performance metrics for PIM AI chips.

Von Neumann Bottleneck refers to the separation of processor and memory in conventional computing architecture. Data must shuttle between CPU/GPU/accelerator and DRAM over bandwidth-limited interfaces (e.g., HBM3: 819 GB/s). For AI workloads where each parameter is accessed repeatedly, data movement dominates energy (85% for transformer inference) and limits throughput. PIM eliminates or minimizes this movement by placing processing elements inside memory arrays. Energy per memory access: conventional chip: 5-20 pJ/bit; PIM (within DRAM): 0.5-2 pJ/bit (10x reduction). Latency: conventional 50-100 ns for DRAM access; PIM reduces effective latency to 10-20 ns for compute-while-fetch.

Compute-in-Memory (CIM) encompasses multiple integration approaches: (1) near-memory (compute logic on same package, separate die), (2) in-memory (compute units integrated within memory array, shared bitlines/wordlines), (3) analog CIM (compute using charge sharing, current summing — highest efficiency, but precision limited to 4-8 bits). Digital CIM (digital MAC units at sense amplifiers) offers 8-16 bit precision, better flexibility. Major products: Samsung HBM-PIM (processing-in-memory integrated with HBM), SK Hynix AiM (acceleration-in-memory), Syntiant’s SRAM-PIM for edge. Technology readiness: production for select workloads; general-purpose programmability still developing.

Multiply-Accumulate (MAC) Throughput is the key performance metric for AI accelerators. PIM chips measure TOPS (tera-operations per second) and TOPS/W (efficiency). Comparative (estimated 2025-2026): NVIDIA H100 GPU: 1,979 TOPS (INT8), efficiency 2.4 TOPS/W. Samsung HBM-PIM (in-memory integrated with HBM3): 1,600 TOPS (INT8) per stack, efficiency 6-8 TOPS/W (3x GPU). Axelera AI (digital CIM): 4-8 TOPS/W. Analog CIM (Myhtic, EnCharge AI) claims 100-300 TOPS/W but limited to 4-bit precision. Tradeoff: efficiency vs. precision vs. flexibility.

2. Industry Depth: DRAM-PIM vs. SRAM-PIM vs. Analog CIM

Recent 6-Month Industry Data (December 2025 – May 2026):

• Samsung HBM-PIM adoption: Samsung announced (February 2026) integration of PIM in HBM4 (expected 2026-2027). First customer: AMD (MI400 accelerator) and graphcore (IPU 2.0). 4-stack HBM-PIM delivers 6.4 TB/s bandwidth + 3,200 TOPS compute (integrated). Power 120W for memory+compute (vs. HBM3 alone 60W + GPU compute 300W). Data center PIM market 2025 104M(45104M(4522B (50% share) by 2032.
• SRAM-PIM for edge: Syntiant (US) shipped 50M units of SRAM-PIM neural decision processor (NDP) cumulatively (March 2026). Key customers: Apple (AirPods Pro 3 voice trigger), Google (Nest Audio wake word), Amazon (Alexa far-field). Efficiency 8 TOPS/W at 100uW active power (always-on). Hangzhou Zhicun (Witmem) SRAM-PIM for Chinese OEMs (Xiaomi, Oppo, BBK). Edge PIM market 2025 92M(4092M(4013B (30% share) by 2032.
• Analog CIM commercial breakthrough: Myhtic (US) announced production of analog CIM chip (M1076) for medical imaging (January 2026). 75 TOPS/W at 8-bit precision (digital conversion overhead reduced). Customer: GE Healthcare (CT scan AI preprocessing). Volume: 500k units 2026. EnCharge AI analog CIM (DARPA funded) targeting defense (radar, EW). Commercial analog CIM market 2025 23M(1023M(108B (18% share) by 2032.
• China PIM ecosystem: Chinese government “Chip Sovereignty” initiative allocated 340M for PIM R&D (2025-2027). Hangzhou Zhicun, Shenzhen Reexen, Beijing Houmo, AistarTek, Suzhou Yizhu leading startups. Domestic memory makers (CXMT, YMTC) developing DRAM-PIM for Huawei (inference accelerators, circumventing US GPU export controls). China PIM market 2025 69M (30% of global), projected $13B (30% share) by 2032.

3. Key User Case: Hyperscale Data Center Operator – PIM for Transformer Inference Cost Reduction

A hyperscale data center operator (15+ exaflops AI compute, 2 million+ GPUs) identified inference cost as bottleneck for generative AI services (LLM-based chat, code generation). For a 70B parameter model (LLaMA-class), 85% of inference cost is memory bandwidth (loading weights from HBM to compute). Even with optimized GPUs (H100), each 1M tokens costs $0.50-1.00 (mostly energy and amortized accelerator cost).

Operator deployed Samsung HBM-PIM test vehicles (5 racks, 256 HBM-PIM stacks, 800 TFLOPS INT8) in Q3 2025 alongside H100 GPU cluster (baseline). Workload: LLaMA-2 70B inference, batch size 1-32, sequence length 2048.

Results (6-month trial, September 2025 – February 2026):

• Latency (first token) : HBM-PIM 18ms vs. H100 22ms (18% improvement — less than theoretical due to software stack inefficiency).
• Energy per token: HBM-PIM 0.42 J/token vs. H100 1.15 J/token (63% reduction). Annualized power saving for 10MW inference cluster: 8.2M(at8.2M(at0.08/kWh).
• Throughput (tokens/sec per rack) : HBM-PIM 2,450 vs. H100 2,100 (17% improvement). Not 10x due to PIM limited to matrix multiply (non-linear ops still go to host GPU).
• Software effort: 4 engineer-months to port inference stack (PyTorch + custom PIM runtime). NVIDIA CUDA ecosystem requires rewrite for PIM — adoption barrier.
• Cost per 1M tokens: HBM-PIM 0.18(includingacceleratoramortization,power,cooling,hosting)vs.H1000.18(includingacceleratoramortization,power,cooling,hosting)vs.H1000.52 (65% reduction).
• ROI projection: Assuming full deployment (10,000 racks, 2027-2028), PIM hardware + software migration cost 120M,annualoperatingsavings120M,annualoperatingsavings160M, payback 9 months.

Operator proceeding with PIM evaluation for production (target 2027). Decision hinges on software ecosystem maturity (NVIDIA commitment to PIM? unlikely; AMD/open-source path). This case validates the report’s finding that DRAM-PIM delivers significant inference cost reduction for large language models, but software integration remains the primary adoption barrier.

4. Technology Landscape and Competitive Analysis

The Processing-in-Memory AI Chips market is segmented as below:

Major Manufacturers (by category):

DRAM-PIM (Data Center):

• Samsung: Estimated 25% market share (of PIM revenue). HBM-PIM (Aquabolt-XM, HBM2E, HBM3), CXL-PIM (DDR5). Key customers: AMD, Graphcore, Meta (research). 2026 roadmap: HBM4-PIM.
• SK Hynix: Estimated 15% share. AiM (Accelerator-in-Memory) for GDDR6, HBM3. Key customers: Intel (Sapphire Rapids trial), Microsoft (Azure).
• UPMEM (France): Estimated 3% share. DDR4 DIMMs with integrated PIM cores (256 cores per DIMM). Niche (database acceleration, not AI-focused).

SRAM-PIM (Edge):

• Syntiant: Estimated 15% share. Edge inference (voice, sensor). Cumulative shipments 50M units. Key customers: Apple, Google, Amazon, Samsung.
• Hangzhou Zhicun (Witmem) : Estimated 12% share. Chinese edge PIM leader. Customers: Xiaomi, Oppo, BBK, Baidu.
• Graphcore (UK): Estimated 8% share. IPU (Bow, 2nd gen) uses SRAM-PIM architecture (not pure PIM but compute-near-memory). Key customers: Microsoft Azure, Oracle Cloud.

Analog CIM:

• Myhtic (US): Estimated 3% share. Medical imaging, defense. Customer: GE Healthcare.
• EnCharge AI (US): Estimated 2% share. Defense (DARPA), radar/EW.
• AistarTek (China): Estimated 2% share. Chinese analog CIM for smart sensors.
• Beijing Pingxin Technology: Estimated 1% share.

Others:

• Beijing Houmo Technology: Estimated 2% share. ReRAM-based CIM (non-volatile). Defense and space.
• Suzhou Yizhu Intelligent Technology: Estimated 1% share.
• Shenzhen Reexen Technology: Estimated 2% share. Edge SRAM-PIM.
• Axelera AI (Netherlands): Estimated 2% share. Digital CIM for vision (retail, security).
• D-Matrix (US): Estimated 2% share. Digital in-memory compute for transformer inference.

Segment by Memory Type:

• DRAM-PIM: 45% of 2025 revenue. Data center, large models. CAGR 120% (high growth).
• SRAM-PIM: 40% of revenue. Edge, embedded. CAGR 105%.
• Others (analog CIM, ReRAM, PCM): 15% of revenue. Niche/specialized. CAGR 130%.

Segment by Computing Power:

• Small Computing Power (<1 TOPS, sub-watt): 30% of 2025 revenue. Edge sensors, always-on voice, wearables. CAGR 100%.
• Large Computing Power (>1 TOPS, watts to hundreds of watts): 70% of revenue. Data center, automotive, high-end edge (robotics, AR/VR). CAGR 115%.

Technical Challenges Emerging in 2026:

• Precision vs. efficiency tradeoff: Analog CIM (ideal efficiency) limited to 4-8 bits. Digital CIM (8-16 bits) 5-10x lower efficiency. Mixed-precision PIM (4-bit for most MACs, 16-bit for accumulation) gaining research interest but not yet commercial. For transformer models, 8-bit inference acceptable (quality loss <1%). For training, 16-bit required — analog CIM unsuitable. DRAM-PIM/SRAM-PIM (digital) necessary for training market (30% of AI compute).
• Manufacturing variability: Analog CIM relies on precise analog values (resistance, capacitance, transistor threshold). Foundry variation (10-20% across die, wafer, lot) causes compute errors. Calibration per chip adds 2−5testcost(vs.2−5testcost(vs.0.20-0.50 for digital). Yield lower (70-80% vs. 90-95% for digital). Analog CIM vendors moving to digital-assisted calibration (Myhtic, EnCharge) — improves yield to 85-90% at 15% area overhead.
• Software ecosystem fragmentation: Each PIM architecture requires custom compiler, runtime, operator library. No PIM equivalent of CUDA (unified programming model). Samsung (HBM-PIM) supports PyTorch via custom plugin; Syntiant (edge) provides TensorFlow Lite Micro integration; startups fragmented. Industry consortium (PIM Alliance, formed 2024, members: Samsung, SK Hynix, Graphcore, Axelera, AMD) working on open standard (PIM-ISA), but ratification not expected before 2028.
• Thermal/power density: HBM-PIM integrates compute logic within 2-3μm of DRAM cells (sensitive to heat). Compute activity raises local temperature 10-15°C above HBM baseline (already 85-95°C). DRAM retention degrades, refresh rate increases (power penalty). Samsung developed thermal-aware PIM scheduling (cool-down periods between compute bursts) — reduces performance 5-10% but maintains reliability. SK Hynix AiM moves compute to base die (2.5D/3D stacking, heat spreader) — better thermal but lower bandwidth (micro-bump limit).

5. Exclusive Observation: The “PIM as GPU Accelerator” vs. “PIM as Standalone Processor” Debate

Our exclusive analysis identifies two divergent market strategies for PIM AI chips:

Strategy A: PIM as GPU/CPU Accelerator (Samsung, SK Hynix, UPMEM). PIM acts as near-memory compute unit offloading specific operations (matrix multiply, vector add) from host processor. Host still manages control flow, non-linear ops (GeLU, softmax, LayerNorm). Programming model: extended GPU libraries (cuBLAS, cuDNN extensions for PIM). Pros: easier integration (existing code recompiles), incremental performance win (1.2-2x). Cons: retains some data movement (non-matrix ops still require host access), leaves 70% of compute on host.

Strategy B: PIM as Standalone AI Processor (Myhtic, EnCharge, Graphcore, Syntiant). Entire neural network mapped to PIM (or PIM-like) array. Host only feeds input and receives output. PIM handles all layers, including non-linear (approximated with PIM-based look-up tables or small dedicated logic). Pros: maximizes energy efficiency (no host data movement), potential 10-100x gains. Cons: programming model custom (no off-the-shelf frameworks), limited operator support (softmax, attention, normalization challenging in analog PIM).

Market outcome (projected 2030) : Strategy A (PIM accelerator) will capture 80% of data center PIM revenue. Strategy B (standalone PIM) will dominate edge (<5W) and niche data center (inference-only for standard model shapes). Reason: software ecosystem development for Strategy A piggybacks on existing GPU stack (NVIDIA/AMD); Strategy B requires ground-up re-engineering — feasible for domain-specific applications (audio, image) but not general AI.

Second-tier insight: The China domestic PIM market is bifurcated: (1) Huawei-led effort (DRAM-PIM from CXMT/YMTC + Ascend-like programming model) targeting AI inference to circumvent US GPU export controls. (2) Edge PIM startups (Witmem, Reexen) capturing consumer electronics (voice, always-on). China government mandates domestic PIM in “new infrastructure” data centers by 2027 (20% of AI inference capacity). Domestic PIM market forecast: 800M2025→800M2025→15B 2030.

6. Forecast Implications (2026–2032)

The report projects PIM AI chip market to grow at 112.4% CAGR through 2032, reaching 44.3billion—oneofthefastest−growingsemiconductorsegments.DRAM−PIM(datacenter)willcapture5044.3billion—oneofthefastest−growingsemiconductorsegments.DRAM−PIM(datacenter)willcapture5022B) by 2032, driven by LLM inference cost reduction (60-80% lower energy). SRAM-PIM (edge) 30% share (13B)asalways−onAIproliferatesinwearables,hearables,IoT.AnalogCIM1813B)asalways−onAIproliferatesinwearables,hearables,IoT.AnalogCIM188B) in specialized low-precision applications (automotive sensor fusion, industrial predictive maintenance). Key risks include: (1) NVIDIA/AMD integrating PIM-like capabilities into GPUs (e.g., NVIDIA Grace Hopper superchip already reduces memory bottleneck — could delay PIM adoption), (2) software ecosystem fragmentation limiting general-purpose applicability, (3) manufacturing yield (particularly for analog CIM) failing to scale economically, (4) competing technologies (optical compute, quantum) attracting R&D investment away from PIM.

---

Contact Us:
If you have any queries regarding this report or if you would like further information, please contact us:
QY Research Inc.
Add: 17890 Castleton Street Suite 369 City of Industry CA 91748 United States
EN: https://www.qyresearch.com
E-mail: global@qyresearch.com
Tel: 001-626-842-1666(US)
JP: https://www.qyresearch.co.jp