In-Memory Computing Patents

In-memory computing / compute-in-memory (CIM) patents cover compute-in-memory architecture innovations; analog-compute innovations; memory-device-compute innovations; and digital-in-memory and data-conversion innovations — with IP held by CIM/analog-AI startups and memory/chip majors (in a field doing computation INSIDE or beside the MEMORY to slash data movement for AI). WHY IN-MEMORY COMPUTING: conventional (von Neumann) computers constantly SHUTTLE data between separate memory and processor — and for AI, this DATA MOVEMENT (not the math) dominates energy and time (the 'von Neumann/memory BOTTLENECK'); IN-MEMORY COMPUTING performs the core AI math — multiply-accumulate (MAC) — right where the weights already live in memory, dramatically cutting data movement, ENERGY, and latency, especially for edge AI inference (high TOPS/W). MAJOR HOLDERS: MYTHIC (analog compute-in-flash), D-MATRIX (digital in-memory compute, Corsair — for datacenter AI inference), SYNTIANT, UNTETHER AI (at-memory), ENCHARGE AI (analog/charge-based), TETRAMEM/KNOWM (memristor), plus SAMSUNG/SK HYNIX (PIM — processing-in-memory in HBM/DRAM) and IBM (analog AI). Compute-in-memory architecture, analog compute, memory-device compute, digital in-memory, and data conversion are the core CIM patent domains — and analog crossbars, memory-device computing, digital CIM, and conversion-overhead solutions are the open whitespace.

Compute-in-memory architecture innovations; analog-compute/crossbar innovations; memory-device-compute innovations; and dataflow/mapping innovations represent core in-memory-computing patent domains — and computing where data lives, using physics to do massively-parallel math, and exploiting specific memory devices are the foundational, high-value capabilities. COMPUTE-IN-MEMORY ARCHITECTURE PATENTS: the core idea — process data IN the memory array (or immediately beside it) rather than fetching it to a distant CPU/GPU — array organization, tiling, and the overall CIM accelerator architecture; CIM architecture is core IP. ANALOG-COMPUTE / CROSSBAR PATENTS: using PHYSICS to compute — in a CROSSBAR array, applying voltages and using Ohm's law (current = voltage × conductance) and Kirchhoff's law (currents sum on a wire) performs an entire multiply-accumulate in ONE analog step, massively parallel and energy-efficient; analog crossbar compute is distinctive, high-value IP (it's the biggest efficiency lever — and the hardest to make accurate). MEMORY-DEVICE-COMPUTE PATENTS: doing CIM in specific memory technologies — SRAM-CIM, embedded FLASH/NOR (Mythic), and emerging non-volatile devices ReRAM/MEMRISTOR, PCM (phase-change), MRAM — each with device-level compute schemes; memory-device compute methods are core (the device choice shapes the whole design). DATAFLOW / WEIGHT-MAPPING PATENTS: mapping neural-network weights onto arrays, tiling large models, and dataflow/scheduling to keep arrays utilized; mapping/dataflow methods are valuable. CIM architecture, analog crossbar compute, and memory-device-specific schemes are the highest-value core IP because computing in the array, using physics for parallel MAC, and exploiting the right memory device are exactly what deliver in-memory computing's energy advantage.

Analog-digital-conversion innovations; calibration/accuracy innovations; digital-in-memory innovations; and PIM and quantization innovations represent additional in-memory-computing patent domains — and overcoming the conversion bottleneck, keeping analog accurate, and the digital alternative are where CIM is made practical. ANALOG-DIGITAL CONVERSION PATENTS: analog CIM needs DACs/ADCs to get signals in and read MAC results out — and these CONVERTERS often dominate the area and energy, undercutting the analog advantage; efficient ADC/DAC design, reduced-precision conversion, and conversion-minimizing architectures are a KEY bottleneck and high-value IP. CALIBRATION / ACCURACY PATENTS: analog compute suffers from device variation, noise, drift, temperature, and nonlinearity — methods for CALIBRATION, error correction, noise-aware training, and robust mapping keep accuracy acceptable; analog-accuracy methods are core (accuracy is analog CIM's Achilles heel). DIGITAL-IN-MEMORY PATENTS: doing DIGITAL MAC in or near the memory (e.g., SRAM-based digital CIM — d-Matrix) — avoiding analog's accuracy/conversion issues at some efficiency cost; digital-CIM architectures are a distinctive, increasingly-favored approach with its own IP. PIM / QUANTIZATION PATENTS: processing-in-memory in commodity DRAM/HBM (Samsung/SK Hynix — bringing compute into memory chips), and low-precision QUANTIZATION schemes that make models fit/run in CIM. Conversion-overhead solutions, analog calibration/accuracy, and digital-in-memory are the highest-value practicality IP because taming ADC/DAC cost, keeping analog accurate, and offering a robust digital path are exactly what determine whether CIM beats GPUs in real deployments.

In-memory computing startup IP strategy must navigate Mythic/d-Matrix/EnCharge and memory-major (Samsung/SK Hynix/IBM) portfolios, decades of crossbar/memristor and analog-compute academic prior art (the concepts are old — practical, manufacturable, accurate implementations are new), the analog-accuracy and ADC/DAC-overhead problems (where most analog CIM struggles — and where defensible IP lives), the analog-vs-digital-CIM strategic split, the memory-device dependence (SRAM vs flash vs ReRAM/PCM/MRAM shapes everything), the software/compiler/mapping stack (mapping models onto CIM is hard — and a moat), the manufacturability/foundry reality and capital intensity, and a landscape where CIM architecture, analog crossbars, memory-device compute, conversion/calibration, and digital CIM are the durable assets; understand that the crossbar concept is well-trodden, so the durable IP is in accurate analog compute, ADC/DAC-overhead reduction, calibration, memory-device-specific schemes, digital CIM, and the mapping/compiler stack — with process/device and software know-how often the real moat, and that TOPS/W, accuracy at low precision, manufacturability, and a working software stack matter as much as patents; identify whitespace in conversion/calibration, digital CIM, and model mapping. IN-MEMORY-COMPUTING STARTUP IP STRATEGY: CROSSBAR/ANALOG CONCEPTS ARE OLD — ACCURATE COMPUTE, CONVERSION/CALIBRATION, MEMORY-DEVICE SCHEMES, DIGITAL CIM, AND MAPPING ARE THE IP: patent accurate analog compute, ADC/DAC-overhead reduction, calibration/error-correction, device-specific CIM, digital-CIM architectures, and weight-mapping/compiler methods; ANALOG ACCURACY + ADC/DAC OVERHEAD ARE THE HARD PROBLEMS AND BEST WHITESPACE: most analog CIM struggles with variation/noise/conversion cost — methods solving these are the most defensible IP; ANALOG VS DIGITAL CIM IS A CORE STRATEGIC SPLIT: analog (max efficiency, accuracy/conversion challenges) vs digital-in-memory (robust, slightly less efficient — d-Matrix) — IP and positioning differ; MEMORY-DEVICE CHOICE SHAPES EVERYTHING: SRAM/flash/ReRAM/PCM/MRAM each enable different CIM — device-specific compute schemes are core IP; THE SOFTWARE/COMPILER/MAPPING STACK IS A MOAT: mapping real models onto CIM arrays (with quantization/calibration) is hard — compiler/mapping IP and know-how differentiate; PIM IN DRAM/HBM IS A PARALLEL TRACK: processing-in-memory in commodity memory (Samsung/SK Hynix) is distinct architectural whitespace; TOPS/W + LOW-PRECISION ACCURACY ARE THE METRICS: the value is energy efficiency at acceptable accuracy — claims/benchmarks should show measured pJ/MAC, TOPS/W, and accuracy; MANUFACTURABILITY/FOUNDRY ALIGNMENT MATTERS: CIM must be fabricable at scale — process-aware/foundry-compatible designs matter; SOFTWARE STACK/DESIGN WINS MATTER AS MUCH AS PATENTS: a usable toolchain and customer qualification drive adoption; WHEN TO PATENT: NOVEL CIM/ANALOG/CONVERSION/CALIBRATION/MAPPING WITH MEASURED EFFICIENCY+ACCURACY: file once a design shows measured results (TOPS/W + energy-per-MAC + accuracy at given precision + conversion overhead + array utilization) — measured TOPS/W, energy-per-MAC, and accuracy-at-precision are the critical in-memory-computing IP metrics; KEY FTO CHECKLIST: Mythic analog compute-in-flash; d-Matrix digital in-memory; EnCharge/Untether/Syntiant/TetraMem; Samsung/SK Hynix PIM (DRAM/HBM); IBM analog AI; compute-in-memory architecture/array/tiling; analog crossbar (Ohm/Kirchhoff MAC); memory-device compute (SRAM/flash/ReRAM-memristor/PCM/MRAM); ADC/DAC conversion overhead/reduction; analog calibration/error-correction/noise-aware training; digital-in-memory architectures; weight mapping/quantization/compiler; TOPS/W/energy-per-MAC; manufacturability/foundry; crossbar/analog prior art.