Edge AI Inference Patents

Edge AI inference patents cover on-device neural processing unit NPU and systolic array accelerator innovations; mobile DSP and tensor core architectural innovations; compiler and runtime graph optimization innovations; and memory bandwidth reduction and dataflow architecture innovations — with IP held by semiconductor companies, AI chip startups, and cloud companies with edge hardware: MAJOR EDGE AI INFERENCE PATENT HOLDERS: APPLE: 2,000+; specific ANE innovations (specific specific Apple Neural Engine ANE: specific specific A17 Pro 35 TOPS from specific specific 3 nm TSMC N3E process from specific specific systolic array MAC unit 16×16 from specific specific INT8 INT4 quantized inference from specific specific 35 TOPS at <0.5 W on-device from specific specific CoreML framework from specific specific BNNS Basic Neural Network Subroutines from specific specific Metal Performance Shaders MPS from specific specific unified memory architecture UMA from specific specific 8 GB shared LPDDR5X from specific specific ProRes video ANE encode/decode from specific specific Neural Engine on every SoC since A11 2017); QUALCOMM: 3,000+; specific Hexagon innovations (specific specific Hexagon 798 NPU: specific specific 75 TOPS Snapdragon 8 Gen 3 from specific specific Micro-tiling architecture from specific specific DMA direct memory access from specific specific Scalar Vector HVX tensor HMX from specific specific CDSP 6 GHz clock from specific specific Hexagon Vector eXtensions HVX 1024-bit SIMD from specific specific Hexagon NN deep learning SDK from specific specific SlimAI model optimization from specific specific Qualcomm AI Engine Direct SDK from specific specific Power Collapse idle 0 power from specific specific Qualcomm AI Hub cloud model optimization); GOOGLE: 500+; SAMSUNG: 1,000+; ARM: 1,000+; HAILO: 200+.

Systolic array processing element PE organization and weight reuse dataflow innovations; compute-in-memory CIM SRAM and RRAM analog crossbar matrix multiplication innovations; and processing-in-memory PIM HBM DRAM near-memory compute innovations represent core edge AI inference patent domains: SYSTOLIC ARRAY AND DATAFLOW PATENTS: GOOGLE; CEREBRAS; GRAPHCORE; GROQ; TENSTORRENT: specific systolic/dataflow innovations (specific specific systolic array: specific specific weight stationary dataflow from specific specific output stationary from specific specific row stationary from specific specific TPU MXU 128×128 bfloat16 from specific specific MAC accumulator PE from specific specific INT8 quantized 8×8 256 MACs/cycle Google Edge TPU from specific specific SIMD/VLIW: specific specific Qualcomm HVX 1024-bit 128 INT8 ops/cycle from specific specific ARM Neon 128-bit 16 INT8/cycle from specific specific Apple AMX accelerate matrix operations from specific specific Apple AMX2 32×32 tile from specific specific dataflow: specific specific Eyeriss MIT row stationary from specific specific NVDLA deep learning accelerator 512 MAC/cycle from specific specific NVDLA modular small-medium-large from specific specific Tenstorrent RISC-V tensix core from specific specific Grayskull Wormhole tile mesh from specific specific Cerebras WSE-3 900K cores wafer-scale from specific specific GraphCore IPU 1,472 tiles Poplar SDK from specific specific Groq TSP tensor streaming 250 TOPSs/chip); COMPUTE-IN-MEMORY PATENTS: MYTHIC AI; ANALOG INFERENCE; IBM; IMEC: specific CIM innovations (specific specific SRAM CIM: specific specific 6T SRAM bitcell AND gate from specific specific 8T non-destructive read from specific specific 256-bit row activation from specific specific MAC in column BL voltage swing from specific specific Mythic AI analog RRAM: specific specific flash cell analog weight from specific specific vector-matrix multiply in memory from specific specific 25 TOPS/W at INT4 from specific specific RRAM synaptic array: specific specific HfOₓ HRS/LRS conductance from specific specific delta-weight update from specific specific Divarix analog resistive inference from specific specific PCM analog multi-level cell from specific specific IBM RBM 3 bits/cell from specific specific HBM processing-in-memory PIM: specific specific Samsung HBM-PIM Aquabolt-XL from specific specific AI computation in DRAM stack from specific specific 2× bandwidth reduction from specific specific near-memory compute: specific specific UPMEM DRAM PIM rank 2,048 DPU from specific specific 1 TB/s peak PIM bandwidth from specific specific Micron CXL memory accelerator); SPARSITY AND STRUCTURED PRUNING PATENTS: NVIDIA; QUALCOMM; HAILO; SAMBANOVA: specific sparsity innovations (specific specific structured pruning: specific specific NVIDIA 2:4 fine-grained sparsity from specific specific 2 non-zero per 4 weights from specific specific 2× speedup A100 Ampere from specific specific sparse tensor core 50% sparsity from specific specific Hailo structural sparsity: specific specific zero-skip zero operand detection from specific specific 0.5 W idle Hailo-8 from specific specific 26 TOPS 2.5 W peak from specific specific unstructured pruning: specific specific lottery ticket hypothesis Frankle-Carlin from specific specific iterative magnitude threshold from specific specific sparse BLAS CSR CSC format).

Post-training quantization PTQ and quantization-aware training QAT INT4 INT8 innovations; knowledge distillation teacher-student soft label and feature matching innovations; and TinyML MCU deployment neural architecture search NAS and layer fusion innovations represent additional edge AI inference patent domains: MODEL COMPRESSION PATENTS: GOOGLE; QUALCOMM; APPLE; HUAWEI: specific compression innovations (specific specific quantization: specific specific INT8 post-training PTQ calibration from specific specific min-max histogram percentile calibration from specific specific GPTQ Hessian second-order PTQ LLM from specific specific QAT quantization-aware training from specific specific fake quantize straight-through estimator STE from specific specific INT4 GPTQ AWQ 4-bit weight from specific specific INT4 activations KV cache from specific specific weight-only W4A16 LLM from specific specific GGUF GGML Q4_K_M Q8_0 formats from specific specific ONNX quantize_static QOperator mode from specific specific QDQ quantize-dequantize node graph from specific specific knowledge distillation: specific specific Hinton soft label KD temperature T=4 from specific specific FitNets feature mimic intermediate layer from specific specific Attention Transfer AT Gram matrix from specific specific CRD contrastive representation distillation from specific specific PKD patient KD layer-wise BERT from specific specific data-free KD: specific specific DAFL data-free DFKD GAN synthetic from specific specific model pruning: specific specific L1 L2 weight magnitude from specific specific Taylor first-order approximation from specific specific MobileNet v3 SE depthwise separable from specific specific EfficientNet-Lite NAS compound scaling); TINYML PATENTS: ETA COMPUTE; SYNTIANT; EDGE IMPULSE; ARM: specific TinyML innovations (specific specific ARM Cortex-M55 Helium: specific specific M-profile vector extension MVE from specific specific 128-bit SIMD 8 INT8/cycle from specific specific TFLite Micro deployment from specific specific TensorFlow Lite Micro TFLM from specific specific FlatBuffer schema 10-50 kB from specific specific CMSIS-NN ARM NN library from specific specific static memory no malloc from specific specific Syntiant NDP120: specific specific neural decision processor from specific specific 0.07 mW always-on keyword from specific specific 8 kB SRAM 256 kB flash from specific specific Eta Compute ECM3532: specific specific SyCl sensor processor from specific specific 20 μW wake-word MCU from specific specific 8 MHz ARM M3 from specific specific ONNX to C code transpiler from specific specific Edge Impulse EON compiler from specific specific neural architecture search NAS: specific specific MobileNetV3 NetAdapt automated pruning from specific specific MnasNet mobile NAS ImageNet from specific specific ProxylessNAS latency-aware edge from specific specific MCUNet TinyML NAS 1 MB SRAM from specific specific layer fusion: specific specific conv+BN+ReLU fusion from specific specific operator fusion TFLite from specific specific MLIR affine loop fusion); RUNTIME AND COMPILER PATENTS: QUALCOMM; APPLE; GOOGLE; MICROSOFT: specific runtime innovations (specific specific CoreML compilation: specific specific mlpackage .mlmodel FP16 INT8 from specific specific ANE graph partition from specific specific compute units CPU GPU ANE from specific specific TFLite delegate: specific specific NNAPI Android Neural Networks API from specific specific GPU delegate OpenCL 2.0 from specific specific Hexagon delegate DSP from specific specific XNNPACK optimized CPU kernels from specific specific OpenVINO IR: specific specific model optimizer topology from specific specific inference engine CPU GPU VPU FPGA from specific specific INT8 symmetric zero-point from specific specific ONNX Runtime: specific specific execution provider EP from specific specific QNN EP Qualcomm from specific specific CoreML EP Apple from specific specific TensorRT: specific specific FP16 INT8 dynamic range calibration from specific specific kernel auto-tuning PTQ).

Edge AI inference startup IP strategy must navigate Apple ANE systolic array patents (2,000+), Qualcomm Hexagon NPU and SlimAI model optimization patents (3,000+), Google Edge TPU and TPU systolic array patents (500+), ARM Cortex-M55 Helium MVE patents (1,000+), and NVIDIA sparse tensor core patents (3,000+); understand that Qualcomm holds the broadest mobile NPU and DSP IP portfolio through Hexagon and Hexagon NN patents; identify whitespace in novel compute-in-memory CIM SRAM or analog RRAM multiply-accumulate MAC array innovations, novel ultra-low-power TinyML MCU neural architecture search innovations, novel structured sparsity patterns beyond NVIDIA 2:4, and novel multi-modal on-device inference (vision-language LLM) with novel KV cache compression — while understanding that the edge AI chip market is projected to exceed $25B by 2027 driven by Apple M-series, Snapdragon 8 Gen 3, and MCU-class deployments: EDGE AI INFERENCE STARTUP IP STRATEGY: UNDERSTAND THE EDGE AI INFERENCE PATENT LANDSCAPE — QUALCOMM HEXAGON AND APPLE ANE HOLD BROAD MOBILE NPU AND SYSTOLIC ARRAY IP: Qualcomm Hexagon patents (3,000+) cover DSP tensor HVX HMX architecture, Micro-tiling dataflow, Hexagon NN compiler, and SlimAI model optimization — new mobile NPU entrants need novel dataflow architecture (row stationary, output stationary variant), novel SRAM organization, or novel ISA; NOVEL CIM ANALOG RRAM AND ULTRA-LOW-POWER TINYML INNOVATIONS ARE HIGHEST-VALUE LEAST-CONSOLIDATED IP: After Mythic AI analog RRAM and Syntiant NDP always-on 0.07 mW deployments, novel analog in-memory inference (PCM, OTS selector, RRAM crossbar array beyond HfOₓ), novel sub-μW wake-word MCU, and novel MCUNet-class NAS for 256 kB SRAM represent less consolidated patent territory vs. Qualcomm/Apple; NOVEL STRUCTURED SPARSITY BEYOND NVIDIA 2:4 AND NOVEL QUANTIZATION FOR SUB-4-BIT LLM EDGE INFERENCE ARE PATENT-VIABLE: NVIDIA holds 2:4 fine-grained sparsity 2× speedup patent — novel N:M sparsity patterns (1:4, 1:8), novel mixed-precision quantization (W2A4, W1A8), novel KV-cache compression for LLM edge, and novel hardware-software co-optimization for sub-1B parameter on-device LLM represent specific patentable innovations; WHEN TO PATENT IN EDGE AI INFERENCE: NOVEL ACCELERATOR WITH MEASURED TOPS/W AND AREA EFFICIENCY: specific novel edge AI inference chip or algorithm (specific specific architecture type + specific specific process node nm + specific specific INT8 TOPS + specific specific peak power W + specific specific TOPS/W + specific specific footprint mm²) with specific measured performance vs. specific Apple A17 Pro ANE 35 TOPS 0.5 W 70 TOPS/W or specific Hailo-8 26 TOPS 2.5 W 10.4 TOPS/W baseline — measured TOPS, TOPS/W energy efficiency, and area efficiency TOPS/mm² vs. ANE, Hexagon, Edge TPU, or Hailo-8 baseline is the critical edge AI inference IP metric; KEY FTO CHECKLIST: Apple ANE A17 Pro 35 TOPS systolic MAC 16×16 INT8 CoreML BNNS Metal 0.5 W UMA LPDDR5X; Qualcomm Hexagon 798 75 TOPS Micro-tiling HVX 1024-bit HMX SlimAI AI Engine Direct; Google Edge TPU 4 TOPS INT8 systolic 8×8 256 MAC Coral; ARM Cortex-M55 Helium MVE 128-bit CMSIS-NN TFLite Micro; NVIDIA 2:4 sparsity 2× Ampere sparse tensor core; Hailo-8 26 TOPS structural sparsity zero-skip 2.5 W; Mythic AI RRAM analog 25 TOPS/W INT4; Syntiant NDP120 0.07 mW 8kB; MCUNet TinyML NAS 256kB; GPTQ AWQ W4A16 LLM quantization.