90 citations · 271 across the 49 of their papers we have counts for
6 papers · 1 filter
A Computationally Efficient Neural Video Compression Accelerator Based on a Sparse CNN-Transformer Hybrid Network
Siyu Zhang, Wendong Mao, Huihong Shi +1
Video compression is widely used in digital television, surveillance systems, and virtual reality. Real-time video decoding is crucial in practical scenarios. Recently, neural vide…
Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design
Chao Fang, Wei Sun, Aojun Zhou +1
Sparse training is one of the promising techniques to reduce the computational cost of DNNs while retaining high accuracy. In particular, N:M fine-grained structured sparsity, wher…
A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edge
Longwei Huang, Chao Fang, Qiong Li +2
Extreme edge platforms, such as in-vehicle smart devices, require efficient deployment of quantized deep neural networks (DNNs) to enable intelligent applications with limited amou…
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution
Minghao She, Wendong Mao, Huihong Shi +1
Nowadays, deep learning based methods have demonstrated impressive performance on ideal super-resolution (SR) datasets, but most of these methods incur dramatically performance dro…
PREFENDER: A Prefetching Defender against Cache Side Channel Attacks as A Pretender
Luyi Li, Jiayi Huang, Lang Feng +1
Cache side channel attacks are increasingly alarming in modern processors due to the recent emergence of Spectre and Meltdown attacks. A typical attack performs intentional cache a…
PDPU: An Open-Source Posit Dot-Product Unit for Deep Learning Applications
Qiong Li, Chao Fang, Zhongfeng Wang
Posit has been a promising alternative to the IEEE-754 floating point format for deep learning applications due to its better trade-off between dynamic range and accuracy. However,…