6 citations · 19 across the 18 of their papers we have counts for
18 papers
AccLLM: Accelerating Long-Context LLM Inference Via Algorithm-Hardware Co-Design
Yanbiao Liang, Huihong Shi, Haikuo Shao +1
Recently, large language models (LLMs) have achieved huge success in the natural language processing (NLP) field, driving a growing demand to extend their deployment from the cloud…
TaQ-DiT: Time-aware Quantization for Diffusion Transformers
Xinyan Liu, Huihong Shi, Yang Xu +1
Transformer-based diffusion models, dubbed Diffusion Transformers (DiTs), have achieved state-of-the-art performance in image and video generation tasks. However, their large model…
M-ViT: Accelerating Hybrid Vision Transformers with Two-Level Mixed Quantization
Yanbiao Liang, Huihong Shi, Zhongfeng Wang
Although Vision Transformers (ViTs) have achieved significant success, their intensive computations and substantial memory overheads challenge their deployment on edge devices. To…
NASH: Neural Architecture and Accelerator Search for Multiplication-Reduced Hybrid Models
Yang Xu, Huihong Shi, Zhongfeng Wang
The significant computational cost of multiplications hinders the deployment of deep neural networks (DNNs) on edge devices. While multiplication-free models offer enhanced hardwar…
Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration
Zhongzhi Yu, Zheng Wang, Yonggan Fu +3
Attention is a fundamental component behind the remarkable achievements of large language models (LLMs). However, our current understanding of the attention mechanism, especially r…
ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization
Haoran You, Yipin Guo, Yichao Fu +6
Large language models (LLMs) have shown impressive performance on language tasks but face challenges when deployed on resource-constrained devices due to their extensive parameters…
P-ViT: Power-of-Two Post-Training Quantization and Acceleration for Fully Quantized Vision Transformer
Huihong Shi, Xin Cheng, Wendong Mao +1
Vision Transformers (ViTs) have excelled in computer vision tasks but are memory-consuming and computation-intensive, challenging their deployment on resource-constrained devices.…
Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision Transformer
Huihong Shi, Haikuo Shao, Wendong Mao +1
Motivated by the huge success of Transformers in the field of natural language processing (NLP), Vision Transformers (ViTs) have been rapidly developed and achieved remarkable perf…
An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT
Haikuo Shao, Huihong Shi, Wendong Mao +1
Vision Transformers (ViTs) have achieved significant success in computer vision. However, their intensive computations and massive memory footprint challenge ViTs' deployment on em…
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…
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…
ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer
Haoran You, Huihong Shi, Yipin Guo +1
Vision Transformers (ViTs) have shown impressive performance and have become a unified backbone for multiple vision tasks. However, both the attention mechanism and multi-layer per…
Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstruction
Sixu Li, Chaojian Li, Wenbo Zhu +6
Neural Radiance Field (NeRF) based 3D reconstruction is highly desirable for immersive Augmented and Virtual Reality (AR/VR) applications, but achieving instant (i.e., < 5 seconds)…
ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention
Jyotikrishna Dass, Shang Wu, Huihong Shi +4
Vision Transformer (ViT) has emerged as a competitive alternative to convolutional neural networks for various computer vision applications. Specifically, ViT multi-head attention…
NASA: Neural Architecture Search and Acceleration for Hardware Inspired Hybrid Networks
Huihong Shi, Haoran You, Yang Zhao +2
Multiplication is arguably the most cost-dominant operation in modern deep neural networks (DNNs), limiting their achievable efficiency and thus more extensive deployment in resour…
ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design
Haoran You, Zhanyi Sun, Huihong Shi +6
Vision Transformers (ViTs) have achieved state-of-the-art performance on various vision tasks. However, ViTs' self-attention module is still arguably a major bottleneck, limiting t…
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
Haoran You, Baopu Li, Huihong Shi +2
Neural networks (NNs) with intensive multiplications (e.g., convolutions and transformers) are capable yet power hungry, impeding their more extensive deployment into resource-cons…
Max-Affine Spline Insights Into Deep Network Pruning
Haoran You, Randall Balestriero, Zhihan Lu +6
In this paper, we study the importance of pruning in Deep Networks (DNs) and the yin & yang relationship between (1) pruning highly overparametrized DNs that have been trained from…