Why the Von Neumann Bottleneck Is Now a Crisis

The von Neumann bottleneck — the growing latency and energy penalty of moving data between physically separated CPU and DRAM — now dominates execution time for machine learning, database, and genomic workloads. Near memory computing (NMC) architectures address this directly by placing computation physically close to, or directly within, memory arrays, reducing or eliminating the data movement penalty entirely.

Three broad sub-domains appear consistently across patent and literature records in this dataset. Processing-In-Memory (PIM) / Near-Memory Processing (NMP) places logic adjacent to DRAM, high-bandwidth memory (HBM), or solid-state storage, enabling bulk data operations without full round-trips to the CPU. Compute-In-Memory (CIM) executes arithmetic operations — particularly vector-matrix multiplication — directly within memory bit-cells, eliminating off-chip data transfers entirely. Logic-In-Memory (LIM) embeds Boolean and higher-order logic operations within or immediately adjacent to storage cells, enabling reconfigurable computation with minimal area overhead.

Core mechanisms described across retrieved results include analog vector-matrix multiplication (VMM) via crossbar arrays, XNOR-based binary operations in SRAM, content-addressable memory (CAM) search operations, and time-domain computation. According to IEEE, memory bandwidth constraints have become one of the defining bottlenecks in modern processor design, a finding consistent with the research directions documented in this dataset.

Four Technology Clusters Defining Near Memory Computing

The near memory computing architecture landscape in this dataset organises into four distinct technology clusters, each with different memory device foundations, performance trade-offs, and maturity levels. No single memory technology dominates across all performance metrics — making technology selection the critical design decision for any R&D team entering this space.

Cluster 1: RRAM/Memristor-Based Analog Compute-In-Memory

The largest cluster in this dataset centres on resistive RAM (RRAM/ReRAM/memristor) crossbar arrays performing analog vector-matrix multiplication. Synaptic weights are encoded as device conductances; current summation along bit lines implements the dot product natively via Ohm’s and Kirchhoff’s laws. Stanford University’s 2022 full chip demonstration achieved high energy efficiency, versatility across models, and software-comparable inference accuracy simultaneously — a critical milestone for the field. Research from Politecnico di Milano (2021) showed that gate-controlled pulses minimise conductance variation for analog weight storage in HfO₂-based RRAM, directly addressing the cycle-to-cycle variation that is the cluster’s primary reliability challenge.

Cluster 2: SRAM-Based Digital Compute-In-Memory

SRAM-based CIM modifies or augments standard 6T, 8T, or 9T bit-cell arrays to perform Boolean or multi-bit arithmetic operations in-place, leveraging the ubiquity of SRAM in production CMOS processes. Purdue University’s 2020 IMAC work demonstrated parallel dot products within unmodified 6T SRAM — a critical milestone for CMOS compatibility. Illinois Institute of Technology’s 2021 8T SRAM design achieved higher energy efficiency through a single-ended cell with decoupled read/write paths without requiring extra transistors. Kwangwoon University’s 2021 work combined 9T CiM SRAM with monolithic 3D (M3D) integration to reduce 2D wire length and improve XNOR-and-accumulation performance for binary neural networks.

Cluster 3: Emerging Non-Volatile Memory CIM

A third cluster exploits STT-MRAM, SOT-MRAM, FeFET, phase-change memory (PCM), and magnetic skyrmion racetrack memories. University of Notre Dame’s 2021 FeFET-based multi-bit CAM achieved 98.34% accuracy on few-shot learning benchmarks, competitive with floating-point software — in a single-step in-memory nearest neighbor search. National Taiwan University’s 2022 SOT-MRAM CIM used a distributed arithmetic algorithm to reduce excessive read/write cycles in CNN inference. Politecnico di Torino’s 2021 skyrmion-based racetrack memory LIM supported advanced max/min search operations with validated physical simulations showing strong area and energy efficiency.

Cluster 4: Near-Memory Processing with HBM and Storage

A distinct cluster places processing logic near but outside memory arrays — within HBM stacks, hybrid memory cubes (HMC), or smart SSDs. Carnegie Mellon University’s 2021 FPGA+HBM work demonstrated large speedups and energy savings over IBM POWER9 systems for genome analysis pre-alignment filtering and weather prediction. Harvard University’s RecSSD achieved 2× end-to-end latency reduction versus commercial off-the-shelf SSDs across eight industry-representative recommendation inference models.

From Theory to Silicon: The Innovation Timeline 2014–2025

The near memory computing field’s evolution spans roughly 2014–2025, with a clear acceleration inflection around 2019–2022 — the period when circuit-level demonstrations moved from simulation to working silicon, and when the first commercial SoC integration efforts appeared.

The pre-2017 foundational period established theoretical underpinnings through early memcomputing concepts (Universitat Autònoma de Barcelona, 2014) and ReRAM-based PIM surveys (IIT Hyderabad, 2018). Memory modeling tools like DESTINY (IIT Hyderabad, 2017) enabled cross-technology design space exploration, providing the evaluation infrastructure that subsequent experimental work depended on.

The 2018–2020 rapid expansion period saw diversification across memory technologies and the first significant circuit-level demonstrations. Purdue University’s 2020 IMAC work — demonstrating computation within standard 6T SRAM without bitcell modification — was a critical milestone for CMOS compatibility, as it showed that CIM need not require exotic process changes.

“Princeton University’s scalable multi-core IMC array patents (2023–2025) represent a leading IP position in the white space of interconnecting CIM tiles beyond sub-128 Kb — the field’s defining unsolved challenge.”

The 2023–2025 productization phase is characterised by scalable architectures and large-capacity systems. Princeton University’s active JP patent filings describe programmable IMC cores interconnected by configurable on-chip networks, directly addressing the scaling limitation that most prior demonstrations were constrained to sub-128 Kb arrays. Samsung Electronics’ filings target data deduplication and write-path management in physical memory spaces, signalling a transition toward AI-workload-optimised memory subsystem architecture at data centre scale.

Emerging directions in the 2022–2025 period also include cryogenic CIM (Georgia Tech, 2022) targeting quantum computing co-processor integration at 4 K using 28-nm cryogenic transistor models, and the expansion of CIM beyond AI inference toward high-precision numerical workloads such as partial differential equations (Fudan University, 2022) — substantially broadening the total addressable market.

Where Near Memory Computing Delivers Real-World Gains

Neural network inference is the dominant application domain in this dataset, but near memory computing’s addressable market extends across five distinct workload categories — each with different latency, energy, and precision requirements that map to different NMC architecture choices.

AI and Machine Learning Inference

CIM architectures map weight matrices to device conductance arrays, enabling massively parallel multiply-accumulate (MAC) operations. Representative work includes Stanford’s RRAM-CIM chip for CNN inference, Kwangwoon University’s M3D binary neural network accelerator, and Harvard’s RecSSD for recommendation inference embedding tables. The RecSSD result — 2× end-to-end latency reduction versus commercial off-the-shelf SSDs across eight industry-representative models — is particularly significant because it was achieved with near-storage processing, which involves less analog complexity than full RRAM CIM.

Edge Computing and IoT

Multiple results target ultra-low-power inference at the edge. Singapore University of Technology and Design’s 2022 work explicitly targets on-edge CNN training using RRAM NAND/NOR circuits. The University of Modena’s 2021 work targets reconfigurable edge hardware using 1T1R crossbar arrays supporting multiple computing paradigms for logic and binarized neural networks.

Database Acceleration, Genomics, and Hyperdimensional Computing

Database acceleration, compression, encryption, and encoding are explicitly covered as NMC use cases in a 2023 review from Ain Shams University. University of Edinburgh’s 2022 work targets terabyte-scale graph analytics with non-volatile RAM. Carnegie Mellon University’s 2021 FPGA+HBM work directly targets genome sequence pre-alignment filtering, demonstrating large speedups and energy savings over IBM POWER9 systems. IBM Research Zürich’s 2021 work targets military multi-domain operations using in-memory hyperdimensional computing for distributed device and service discovery — a niche but strategically significant application demonstrating NMC’s reach beyond commercial AI.

Geographic and Assignee Landscape: Who Holds the IP

Innovation in this dataset is broadly distributed across academic institutions rather than concentrated in a few industrial players — but Samsung Electronics, Applied Materials, and Stanford University represent the clearest bridges between research demonstration and commercial product, and warrant close monitoring by IP strategists.

United States dominates by institution count, with contributions from Stanford University, Carnegie Mellon University, Harvard University, Purdue University, Georgia Institute of Technology, MIT, Drexel University, University of Notre Dame, Oak Ridge National Laboratory, Argonne National Laboratory, and Sandia National Laboratories — reflecting strong federally funded research in PIM and CIM.

Asia is the most active region for patent filings. Samsung Electronics (KR/JP) holds recent active patents on large-capacity in-memory computing memory systems. Princeton University (JP jurisdiction filings) holds active patents on scalable IMC array architectures interconnected via on-chip networks. Tsinghua University / Beijing ICFC (CN) is the most prominent Chinese academic institution. China Electronics Technology Group Corporation No.58 Research Institute (CN) contributes 8-bit resistive memory computing core designs targeting edge AI.

Europe is represented by Politecnico di Torino (IT) — the most prolific European institution in this dataset, covering LIM, skyrmion memory, and SIMPLY architectures — along with CEA-LETI Grenoble (FR), IBM Research Zürich (CH), Politecnico di Milano (IT), and University of Edinburgh (UK). As noted by WIPO, cross-jurisdictional patent filing patterns in semiconductor memory technologies increasingly reflect productization intent rather than purely defensive IP strategy — a pattern consistent with Samsung’s JP filings documented here.

Strategic Implications for R&D and IP Teams

Five strategic implications emerge from this dataset for R&D leaders, IP strategists, and product teams working in or adjacent to near memory computing architecture.

Memory Technology Selection Is the Critical Design Decision

No single memory technology — RRAM, SRAM, STT-MRAM, SOT-MRAM, FeFET, or PCM — dominates across all performance metrics. Density, endurance, analog precision, and CMOS integration maturity trade off differently for inference, training, edge, and data centre contexts. R&D teams must conduct application-specific cross-technology evaluation using frameworks like NVMExplorer (Tufts University, 2022) or MHSim (Huazhong University of Science and Technology, 2022) before committing to a device platform. According to Nature, multi-state non-volatile memory devices remain an active area of materials research precisely because no existing technology fully satisfies the precision, endurance, and density requirements of large-scale analog CIM simultaneously.

Scaling Beyond 128 Kb Is the Field’s Defining Unsolved Challenge

Multiple sources explicitly identify that most CIM demonstrations remain sub-128 Kb due to analog non-idealities worsening at scale and the absence of established architectures for interconnecting CIM tiles. Princeton’s scalable multi-core IMC array patents (2023–2025) represent a leading IP position in this white space. IP strategists should map freedom-to-operate carefully in this area before committing R&D resources to tile-interconnect architectures.

Samsung’s Productization Patent Activity Warrants Close Monitoring

Samsung Electronics holds multiple active recent patents in JP jurisdiction covering large-capacity in-memory computing memory systems, distinct from its established DRAM/NAND product lines. This signals potential product roadmap activity that could reshape the competitive landscape for AI memory systems by 2026–2027. Competitive intelligence teams should establish automated monitoring for new Samsung filings in this category.

Near-Storage Computing Is an Underinvested Complement

Results from Harvard (RecSSD), Carnegie Mellon (FPGA+HBM), and Ain Shams University indicate that near-storage processing for recommendation inference, genome analysis, and database acceleration achieves substantial latency and energy gains with less analog complexity than full CIM. This represents a lower-risk near-term insertion point for product teams compared to RRAM analog CIM, and appears underrepresented in current patent filing activity relative to its performance potential.

Tooling and Simulation Infrastructure Is Both a Bottleneck and a Business Opportunity

The NMPO framework (Eindhoven, 2021), MHSim (Huazhong, 2022), and NVMExplorer (Tufts, 2022) all highlight that the lack of real NMC hardware forces reliance on slow, often inaccurate simulators, delaying design cycles. According to OECD analysis of semiconductor R&D productivity, simulation toolchain quality is a significant determinant of time-to-tape-out in emerging memory architectures. Investment in fast, validated cross-stack NMC simulation tools — or in early silicon access programs — will provide decisive competitive intelligence advantages for both IP strategists and product development teams.

“Investment in fast, validated cross-stack NMC simulation tools will provide decisive competitive intelligence advantages — because the absence of real NMC hardware still makes simulation the primary evaluation vehicle for most R&D teams.”

The PatSnap Eureka platform provides AI-native patent landscape analysis across all four NMC technology clusters, enabling R&D and IP teams to identify white space, map freedom-to-operate, and monitor competitor filing activity in real time. The PatSnap IP intelligence suite supports cross-jurisdictional monitoring of the Samsung and Princeton filing activity documented in this landscape.