59 citations · 112 across the 27 of their papers we have counts for
8 papers · 1 filter
Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning
Liwei Ni, Zonglin Yang, Jiaxi Zhang +4
Rewriting is a common procedure in logic synthesis aimed at improving the performance, power, and area (PPA) of circuits. The traditional reconvergence-driven And-Inverter Graph (A…
Cross-Layer Optimization for Fault-Tolerant Deep Learning
Qing Zhang, Cheng Liu, Bo Liu +4
Fault-tolerant deep learning accelerator is the basis for highly reliable deep learning processing and critical to deploy deep learning in safety-critical applications such as avio…
SPFL: A Self-purified Federated Learning Method Against Poisoning Attacks
Zizhen Liu, Weiyang He, Chip-Hong Chang +3
While Federated learning (FL) is attractive for pulling privacy-preserving distributed training data, the credibility of participating clients and non-inspectable data pose new sec…
DeepBurning-MixQ: An Open Source Mixed-Precision Neural Network Accelerator Design Framework for FPGAs
Erjing Luo, Haitong Huang, Cheng Liu +5
Mixed-precision neural networks (MPNNs) that enable the use of just enough data width for a deep learning task promise significant advantages of both inference accuracy and computi…
Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance
Xinghua Xue, Cheng Liu, Bo Liu +6
Winograd is generally utilized to optimize convolution performance and computational efficiency because of the reduced multiplication operations, but the reliability issues brought…
MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing
Haitong Huang, Cheng Liu, Bo Liu +3
To ensure resilient neural network processing on even unreliable hardware, comprehensive reliability analysis against various hardware faults is generally required before the deep…
ChipGPT: How far are we from natural language hardware design
Kaiyan Chang, Ying Wang, Haimeng Ren +5
As large language models (LLMs) like ChatGPT exhibited unprecedented machine intelligence, it also shows great performance in assisting hardware engineers to realize higher-efficie…
Soft Error Reliability Analysis of Vision Transformers
Xinghua Xue, Cheng Liu, Ying Wang +5
Vision Transformers (ViTs) that leverage self-attention mechanism have shown superior performance on many classical vision tasks compared to convolutional neural networks (CNNs) an…