Intelligent-Microsystems-Lab / QuantizedSNNsLinks
This repository contains the models and training scripts used in the papers: "Quantizing Spiking Neural Networks with Integers" (ICONS 2020) and "Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators" (ISCAS 2020).
☆13Updated 5 years ago
Alternatives and similar repositories for QuantizedSNNs
Users that are interested in QuantizedSNNs are comparing it to the libraries listed below
Sorting:
- Framework for radix encoded SNN on FPGA☆16Updated 3 years ago
- SATA_Sim is an energy estimation framework for Backpropagation-Through-Time (BPTT) based Spiking Neural Networks (SNNs) training and infe…☆28Updated last year
- MINT, Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks, ASP-DAC 2024, Nominated for Best Paper Award☆15Updated last year
- I will share some useful or interesting papers about neuromorphic processor☆27Updated 9 months ago
- A nest brain simulator based on FPGA(LIF NEURON)☆14Updated 3 years ago
- ☆17Updated 4 years ago
- ☆20Updated 4 years ago
- ☆38Updated 4 years ago
- ReckOn: A Spiking RNN Processor Enabling On-Chip Learning over Second-Long Timescales - HDL source code and documentation.☆91Updated 3 years ago
- SNN on FPGA☆11Updated 3 years ago
- An energy simulation framework for BPTT-based SNN inference and training.☆17Updated 2 years ago
- Pytorch implementation of SEENN (Spiking Early Exit Neural Networks) (NeurIPS 2023)☆17Updated last year
- The official implementation of HPCA 2025 paper, Prosperity: Accelerating Spiking Neural Networks via Product Sparsity☆36Updated 3 months ago
- The CyNAPSE Neuromorphic Accelerator: A Digital Spiking neural network accelerator written in fully synthesizable verilog HDL☆36Updated 6 years ago
- STBP is a way to train SNN with datasets by Backward propagation.Using this Repositories allows you to train SNNS with STBP and quantize …☆30Updated 3 years ago
- [FPL 2021] SyncNN: Evaluating and Accelerating Spiking Neural Networks on FPGAs.☆62Updated 4 years ago
- My name is Fang Biao. I'm currently pursuing my Master degree with the college of Computer Science and Engineering, Si Chuan University, …☆53Updated 2 years ago
- ☆11Updated 6 years ago
- Code for the ISCAS23 paper "The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks"☆11Updated 2 years ago
- training SNN with Resume algorithm☆11Updated 6 years ago
- Benchmark framework of compute-in-memory based accelerators for deep neural network (on-chip training chip focused)☆54Updated 4 years ago
- Hardware implementation of Spiking Neural Network on a PYNQ-Z1 board☆38Updated 6 years ago
- LoAS: Fully Temporal-Parallel Dataflow for Dual-Sparse Spiking Neural Networks, MICRO 2024.☆14Updated 8 months ago
- ☆20Updated 3 years ago
- ☆55Updated 2 years ago
- ☆18Updated 2 years ago
- AFP is a hardware-friendly quantization framework for DNNs, which is contributed by Fangxin Liu and Wenbo Zhao.☆13Updated 4 years ago
- Models and training scripts for "LSTMs for Keyword Spotting with ReRAM-based Compute-In-Memory Architectures" (ISCAS 2021).☆16Updated 4 years ago
- Benchmark framework of compute-in-memory based accelerators for deep neural network☆45Updated 5 years ago
- Quantized Training for Convolutional Neural Networks using Xilinx Brevitas☆12Updated 3 years ago