ciodar / deep-compressionLinks
PyTorch Lightning implementation of the paper Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. This repository allows to reproduce the main findings of the paper on MNIST and Imagenette datasets.
☆29Updated 7 months ago
Alternatives and similar repositories for deep-compression
Users that are interested in deep-compression are comparing it to the libraries listed below
Sorting:
- Official implementation for ECCV 2022 paper LIMPQ - "Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance"☆56Updated 2 years ago
- [IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.☆59Updated last year
- Implementation of "NITI: Training Integer Neural Networks Using Integer-only Arithmetic" on arxiv☆84Updated 2 years ago
- [NeurIPS 2023] ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer☆31Updated last year
- Post-training sparsity-aware quantization☆34Updated 2 years ago
- ☆25Updated 3 years ago
- [ICML 2021] "Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators" by Yonggan Fu, Yonga…☆16Updated 3 years ago
- A collection of research papers on efficient training of DNNs☆70Updated 3 years ago
- Recent Advances on Efficient Vision Transformers☆51Updated 2 years ago
- Torch2Chip (MLSys, 2024)☆53Updated 3 months ago
- ☆76Updated 2 years ago
- Official implementation for the paper "Understanding Hyperdimensional Computing for Parallel Single-Pass Learning"☆20Updated 2 years ago
- ☆42Updated last year
- [CVPR 2024] Offical implementation for A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network☆25Updated 6 months ago
- [ICML 2022] ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks☆16Updated 3 years ago
- μNAS is a neural architecture search (NAS) system that designs small-yet-powerful microcontroller-compatible neural networks.☆80Updated 4 years ago
- Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight…☆63Updated 11 months ago
- ☆18Updated 3 years ago
- Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples [NeurIPS 2021]☆33Updated 3 years ago
- We have implemented a framework that supports developers to structured prune neural networks of Tensorflow Models☆28Updated 8 months ago
- TBNv2: Convolutional Neural Network With Ternary Inputs and Binary Weights☆17Updated 5 years ago
- Binarize convolutional neural networks using pytorch☆146Updated 3 years ago
- Personal Digest of NAS (Under Construction 🛠)☆25Updated 4 years ago
- [ECCV 2022] SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning☆20Updated 3 years ago
- Official implementation of Neurips 2020 "Sparse Weight Activation Training" paper.☆27Updated 3 years ago
- An open-sourced PyTorch library for developing energy efficient multiplication-less models and applications.☆13Updated 5 months ago
- ☆61Updated last month
- Code repo for the paper BiT Robustly Binarized Multi-distilled Transformer☆109Updated 2 years ago
- ☆22Updated last year
- Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge☆10Updated 2 years ago