3 papers
cs.CV2023
MetaGrad: Adaptive Gradient Quantization with Hypernetworks
Kaixin Xu, Alina Hui Xiu Lee, Ziyuan Zhao +3
A popular track of network compression approach is Quantization aware Training (QAT), which accelerates the forward pass during the neural network training and inference. However,…
cs.AR2022
Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile
Renzo Andri, Beatrice Bussolino, Antonio Cipolletta +2
Most of today's computer vision pipelines are built around deep neural networks, where convolution operations require most of the generally high compute effort. The Winograd convol…
cs.CR2021
FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data
Yuxiao Lu, Jie Lin, Chao Jin +4
Homomorphic Encryption (HE), allowing computations on encrypted data (ciphertext) without decrypting it first, enables secure but prohibitively slow Convolutional Neural Network (C…