most citedn-hot: Efficient bit-level sparsity for powers-of-two neural network quantization

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CV2021★ 3 cited

n-hot: Efficient bit-level sparsity for powers-of-two neural network quantization

Yuiko Sakuma, Hiroshi Sumihiro, Jun Nishikawa +2

Powers-of-two (PoT) quantization reduces the number of bit operations of deep neural networks on resource-constrained hardware. However, PoT quantization triggers a severe accuracy…

cs.CV2020

Filter Pre-Pruning for Improved Fine-tuning of Quantized Deep Neural Networks

Jun Nishikawa, Ryoji Ikegaya

Deep Neural Networks(DNNs) have many parameters and activation data, and these both are expensive to implement. One method to reduce the size of the DNN is to quantize the pre-trai…