There are 8 repositories under neuromorphic-computing topic.
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
螺旋熵减系统
Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.
Offical implementation of "Spike-driven Transformer" (NeurIPS2023)
Open source SDK to create applications leveraging event-based vision hardware equipment
Actively developed Hierarchical Temporal Memory (HTM) community fork (continuation) of NuPIC. Implementation for C++ and Python
List of open source neuromorphic projects: SNN training frameworks, DVS handling routines and so on.
螺旋熵减理论
Long short-term memory Spiking Neural Networks
A paper list of spiking neural networks, including papers, codes, and related websites.
This repository will host models, modules, algorithms and applications developed by the INRC Community to run on the Intel Loihi Platform.
Deep learning for spiking neural networks
Spiking-DDPG trains an SNN for energy-efficient mapless navigation on Intel's Loihi neuromorphic processor.
Spikingformer: Spike-driven Residual Learning for Transformer-based Spiking Neural Network
Neuromorphic mathematical optimization with Lava
Official repository of Spiking-FullSubNet, the Intel N-DNS Challenge Algorithmic Track Winner.
Leaky Integrate and Fire (LIF) model implementation for FPGA
Enhancing the Performance of Transformer-based Spiking Neural Networks by SNN-optimized Downsampling with Precise Gradient Backpropagation
PyTorch and Loihi implementation of the Spiking Neural Network for decoding EEG on Neuromorphic Hardware
Low-level Python APIs for Accessing Neuromorphic Devices.
Python implementations and simulations of HP Labs Ion Drift and Yakopcic memristor models.
NeuroMorphic Predictive Model with Spiking Neural Networks (SNN) using Pytorch
Structured clustering for memristive crossbar based neuromorphic architectures
Dynex has also developed a proprietary circuit design, the Dynex Neuromorphic Chip, that complements the Dynex ecosystem and turns any modern G into a neuromorphic computing chip by simulating its equations of motion. This implementation proofs the mathematical model.
With the end of Moore’s law approaching and Dennard scaling ending, the computing community is increasingly looking at new technologies to enable continued performance improvements. A neuromorphic computer is a nonvon Neumann computer whose structure and function are inspired by biology and physics. Today, such systems can be built and operated using existing technology, even at scale, and are capable of outperforming current quantum computers.
[CELL PATTERNS] Official repo of Noisy Spiking Neural Networks
ANN to SNN conversion on land cover and land use classification problem for increased energy efficiency.