Accelerating deep neural network (DNN) inference on resource-limited devices is one of the most important barriers to ensuring a wider and more inclusive adoption. To alleviate this, DNN binary quantization for faster convolution and memory savings is one of the most promising strategies despite its serious drop in accuracy. The present paper therefore proposes a novel binary quantization function based on quantized compressed sensing (QCS). Theoretical arguments conjecture that our proposal preserves the practical benefits of standard methods, while reducing the quantization error and the resulting drop in accuracy.
@article{arxiv.2108.10101,
title = {On the Acceleration of Deep Neural Network Inference using Quantized Compressed Sensing},
author = {Meshia Cédric Oveneke},
journal= {arXiv preprint arXiv:2108.10101},
year = {2021}
}
Comments
3 pages, no figures, paper accepted at Black In AI at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada