24 citations · 72 across the 20 of their papers we have counts for
12 papers · 1 filter
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment
Yonggan Fu, Zhongzhi Yu, Junwei Li +6
Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effective…
MG-Verilog: Multi-grained Dataset Towards Enhanced LLM-assisted Verilog Generation
Yongan Zhang, Zhongzhi Yu, Yonggan Fu +2
Large Language Models (LLMs) have recently shown promise in streamlining hardware design processes by encapsulating vast amounts of domain-specific data. In addition, they allow us…
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…
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…
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
Yonggan Fu, Yang Zhang, Kaizhi Qian +4
Self-supervised learning (SSL) for rich speech representations has achieved empirical success in low-resource Automatic Speech Recognition (ASR) and other speech processing tasks,…
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks
Yonggan Fu, Qixuan Yu, Yang Zhang +4
Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks, i.e., an imperceptible perturbation to the input can mislead DNNs trained on clean images into making…
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…
O-HAS: Optical Hardware Accelerator Search for Boosting Both Acceleration Performance and Development Speed
Mengquan Li, Zhongzhi Yu, Yongan Zhang +2
The recent breakthroughs and prohibitive complexities of Deep Neural Networks (DNNs) have excited extensive interest in domain-specific DNN accelerators, among which optical DNN ac…
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…
Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators
Yonggan Fu, Yongan Zhang, Yang Zhang +2
While maximizing deep neural networks' (DNNs') acceleration efficiency requires a joint search/design of three different yet highly coupled aspects, including the networks, bitwidt…
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…