Neuromorphic Computing Python Packages

pyaer

Low-level Python APIs for Accessing Neuromorphic Devices.

dynex

Dynex SDK

lava-nc

A Software Framework for Neuromorphic Computing

micronet

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

holovec

Holographic vectors you can compute with. Bind structure, bundle sets, unbind components cross NumPy, PyTorch, and JAX.

sc-neurocore

Universal Stochastic Computing Framework for Neuromorphic Hardware — Rust SIMD engine, Python simulation, Verilog RTL, HDC/VSA, SCPN integration

pixelflow-rc

GPU texture reservoir computing — fragment-shader cellular automata with linear readouts for image classification and PDE solving. Honest benchmarks, CPU/OpenGL/CUDA backends.

lava-dl

Deep Learning library for Lava

pyorganoid

A Python package for the simulation of organoids for the purpose of studying Organoid Intelligence (OI) and Organoid Learning (OL).

synaptic-ml

The TensorFlow for neuromorphic computing. Train spiking neural networks and deploy to Intel Loihi 2 at 1000x less energy than GPU.

neuroptimiser

A modular and asynchronous framework for neuromorphic optimisation using spike-driven metaheuristics and heuristic-controlled spiking dynamics.

lens-vpr

LENS: Locational Encoding with Neuromorphic Systems

lava-optimization

Neuromorphic mathematical optimization with Lava

sapicore

A framework for spiking neural network modeling.

pynmsnn

NeuroMorphic Predictive Model with Spiking Neural Networks (SNN) using Pytorch

lava-dnf

Dynamic Neural Fields with Lava

snnax

Spiking neural networks in JAX with equinox.