compute-in-memory

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Here are 10 public repositories matching this topic...

BUAA-CI-LAB / Literatures-on-SRAM-based-CIM

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A reading list for SRAM-based Compute-In-Memory (CIM) research.

accelerator in-memory sram reading-list pim literature cim paper-list process-in-memory compute-in-memory

Updated Oct 29, 2025

EzraWolf / TinyMOA-IHP26a

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IHP26a TinyTapeout implementation of a RISC-V CPU with an integrated SRAM-based compute-in-memory (CIM) accelerator for performing efficient analog matrix multiplications.

machine-learning asic cpu fpga riscv neuromorphic risc-v neuromorphic-computing analog-computing tinytapeout compute-in-memory

Updated Mar 24, 2026
Python

Scophield / snn-structural-evolution

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Minimal PyTorch examples for the four-stage structural evolution from ANN to event-driven SNN: Stage 0 (baseline ANN) → Stage 1 (binarization) → Stage 2 (temporal expansion) → Stage 3 (temporal accumulation) → Stage 4 (reset & sparsity control).

pytorch mnist event-driven snn neuromorphic-computing reram spiking-neural-network ann-to-snn compute-in-memory hardware-aware-ai surrogate-gradient analog-ai

Updated Jun 8, 2026
Python

TimurIbrayev / DUB_Sparsity_for_XBars

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DUB Sparsity for Crossbars

pruning crossbar analog-to-digital-converter process-in-memory hardware-software-codesign compute-in-memory

Updated Mar 21, 2024
Python

EzraWolf / TinyMOA

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LLM inference SoC

cpu riscv soc fpga-soc risc-v asic-design compute-in-memory asic-soc

Updated Jun 25, 2026
Verilog

Joschua-Conrad / PSumSim

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PSumSim: A Simulator for Partial-Sum Quantization in Analog Matrix-Vector Multipliers

numpy histogram neural-networks histograms quantization matrix-vector-multiplication stochastic-simulation stochastic-modeling compute-in-memory partial-sum

Updated May 5, 2026
Python

cpldcpu / DRAM_exploration

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Some experiments to perform parallel data operations (compute-in-memory) on a 1980ies DRAM chip controlled with a CH32V003 RISC-V MCU

dram cim 4164 ch32v003 compute-in-memory

Updated May 20, 2025
C

jakkarin-promsee / bio-analog-cpu

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A bio-inspired analog compute substrate that learns on-chip — online, local, and forward-only. Weights live as analog charge on capacitors (compute-in-memory); an unsupervised forward-only front (SCFF) does ~80% of the work, a small gradient-descent namer the rest. A chip-design bet, not an ML model.