75 citations · 305 across the 39 of their papers we have counts for
15 papers · 1 filter
From Inference Efficiency to Embodied Efficiency: Revisiting Efficiency Metrics for Vision-Language-Action Models
Zhuofan Li, Hongkun Yang, Zhenyang Chen +4
Vision-Language-Action (VLA) models have recently enabled embodied agents to perform increasingly complex tasks by jointly reasoning over visual, linguistic, and motor modalities.…
Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
Chaojian Li, Zhifan Ye, Massimiliano Lupo Pasini +4
Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property…
GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
Yonggan Fu, Yongan Zhang, Zhongzhi Yu +5
The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have dramatically escalated the imperative for specialized AI accelerators. Nonetheless, designing…
ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement
Chaojian Li, Wenwan Chen, Jiayi Yuan +2
Social ambiance describes the context in which social interactions happen, and can be measured using speech audio by counting the number of concurrent speakers. This measurement ha…
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…
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency
Yonggan Fu, Yang Zhao, Qixuan Yu +2
The recent breakthroughs of deep neural networks (DNNs) and the advent of billions of Internet of Things (IoT) devices have excited an explosive demand for intelligent IoT devices…
A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning
Yonggan Fu, Yongan Zhang, Chaojian Li +2
Driven by the explosive interest in applying deep reinforcement learning (DRL) agents to numerous real-time control and decision-making applications, there has been a growing deman…
InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks
Yonggan Fu, Zhongzhi Yu, Yongan Zhang +6
The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT) devices has motivated a tremendous demand for automated solutions to enable fast development and deployment…
HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark
Chaojian Li, Zhongzhi Yu, Yonggan Fu +6
HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous attention by automating the design of DNNs deployed in more resource-constrained daily life device…
SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training
Xiaohan Chen, Yang Zhao, Yue Wang +6
The record-breaking performance of deep neural networks (DNNs) comes with heavy parameterization, leading to external dynamic random-access memory (DRAM) for storage. The prohibiti…
DNA: Differentiable Network-Accelerator Co-Search
Yongan Zhang, Yonggan Fu, Weiwen Jiang +5
Powerful yet complex deep neural networks (DNNs) have fueled a booming demand for efficient DNN solutions to bring DNN-powered intelligence into numerous applications. Jointly opti…
ShiftAddNet: A Hardware-Inspired Deep Network
Haoran You, Xiaohan Chen, Yongan Zhang +5
Multiplication (e.g., convolution) is arguably a cornerstone of modern deep neural networks (DNNs). However, intensive multiplications cause expensive resource costs that challenge…
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation
Yang Zhao, Xiaohan Chen, Yue Wang +6
We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep ne…
DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architectures
Yang Zhao, Chaojian Li, Yue Wang +3
The recent breakthroughs in deep neural networks (DNNs) have spurred a tremendously increased demand for DNN accelerators. However, designing DNN accelerators is non-trivial as it…
Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks
Haoran You, Chaojian Li, Pengfei Xu +6
(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve comp…