11 citations · 31 across the 19 of their papers we have counts for
19 papers
Harvesting AI Computation at the Edge via Generic Approximation
Yihan Wang, Huiru Yan, Luxin Zhang +6
With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge. These chips are typically spe…
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing
Zhehui Wang, Benjamin Chen Ming Choong, Tian Huang +4
Quantum optimization is the most mature quantum computing technology to date, providing a promising approach towards efficiently solving complex combinatorial problems. Methods suc…
LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator
Guoyu Li, Shengyu Ye, Chunyun Chen +6
The emergence of neural network capabilities invariably leads to a significant surge in computational demands due to expanding model sizes and increased computational complexity. T…
Enabling Energy-Efficient Deployment of Large Language Models on Memristor Crossbar: A Synergy of Large and Small
Zhehui Wang, Tao Luo, Cheng Liu +3
Large language models (LLMs) have garnered substantial attention due to their promising applications in diverse domains. Nevertheless, the increasing size of LLMs comes with a sign…
DRIM-ANN: An Approximate Nearest Neighbor Search Engine based on Commercial DRAM-PIMs
Mingkai Chen, Tianhua Han, Cheng Liu +8
Approximate nearest neighbor search (ANNS) is essential for applications like recommendation systems and retrieval-augmented generation (RAG) but is highly I/O-intensive and memory…
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…
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…
Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing System
Hao Lv, Bing Li, Lei Zhang +2
The RRAM-based neuromorphic computing system has amassed explosive interests for its superior data processing capability and energy efficiency than traditional architectures, and t…
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…
Statistical Modeling of Soft Error Influence on Neural Networks
Haitong Huang, Xinghua Xue, Cheng Liu +5
Soft errors in large VLSI circuits pose dramatic influence on computing- and memory-intensive neural network (NN) processing. Understanding the influence of soft errors on NNs is c…
Fault-Tolerant Deep Learning: A Hierarchical Perspective
Cheng Liu, Zhen Gao, Siting Liu +3
With the rapid advancements of deep learning in the past decade, it can be foreseen that deep learning will be continuously deployed in more and more safety-critical applications s…
Winograd Convolution: A Perspective from Fault Tolerance
Xinghua Xue, Haitong Huang, Cheng Liu +3
Winograd convolution is originally proposed to reduce the computing overhead by converting multiplication in neural network (NN) with addition via linear transformation. Other than…
Taming Process Variations in CNFET for Efficient Last Level Cache Design
Dawen Xu, Zhuangyu Feng, Cheng Liu +5
Carbon nanotube field-effect transistors (CNFET) emerge as a promising alternative to CMOS transistors for the much higher speed and energy efficiency, which makes the technology p…
R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors
Dawen Xu, Meng He, Cheng Liu +5
AIoT processors fabricated with newer technology nodes suffer rising soft errors due to the shrinking transistor sizes and lower power supply. Soft errors on the AIoT processors pa…
HyCA: A Hybrid Computing Architecture for Fault Tolerant Deep Learning
Cheng Liu, Cheng Chu, Dawen Xu +5
Hardware faults on the regular 2-D computing array of a typical deep learning accelerator (DLA) can lead to dramatic prediction accuracy loss. Prior redundancy design approaches ty…
EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks
Shengwen Liang, Ying Wang, Cheng Liu +3
Graph neural networks (GNNs) emerge as a powerful approach to process non-euclidean data structures and have been proved powerful in various application domains such as social netw…
Accelerating Generative Neural Networks on Unmodified Deep Learning Processors -- A Software Approach
Dawen Xu, Ying Wang, Kaijie Tu +3
Generative neural network is a new category of neural networks and it has been widely utilized in applications such as content generation, unsupervised learning, segmentation and p…