Models made for Edge Devices and NN Optimizations
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Updated
Oct 25, 2019 - Python
Models made for Edge Devices and NN Optimizations
A toy example of OCTAV algorithm for finding the optimal clipping scalar in the quantization error problem
[EACL 2023 main] This Repository provides a Pytorch implementation of Teacher Intervention: Improving Convergence of Quantization Aware Training for Ultra-Low Precision Transformers
Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training
Implementation of SOMs (Self-Organizing Maps) with neighborhood-based map topologies.
[MicroNet Challenge (NeurIPS 2019 )] "Adjustable Quantization: Jointly Learn the Bit-width and Weight in DNN Training" by Yonggan Fu, Ruiyang Zhao, Yue Wang, Chaojian Li, Haoran You, Zhangyang Wang, Yingyan Lin
IntLLaMA: A fast and light quantization solution for LLaMA
Regularized Classification-Aware Quantization
code for paper: https://arxiv.org/pdf/2203.08080.pdf
Optimizing quantization tables for JPEG2000 codec with significant rate-accuracy performance.
8 bit quantizated Transformer for neural machine translation.
The Secret Inner Workings of Time Exposed by Atomic Clocks
A vector quantization method with binary codes, in PyTorch.
Quantized training using Keras
Optimized CPU Implementation of Llama2-LLM
Efficient Inference techniques implemented in PyTorch for computer vision.
A library to help with the development of AI models with Keras, with a focus on edge / IoT applications. Based originally on https://github.com/yingkaisha/keras-unet-collection
Training neural network weights in a bitwise fashion: https://arxiv.org/abs/2202.09571
Quantization for Object Detection in Tensorflow 2.x
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