24 citations · 85 across the 20 of their papers we have counts for
21 papers
LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models
Dachuan Shi, Yonggan Fu, Xiangchi Yuan +8
Recent advancements in Large Language Models (LLMs) have spurred interest in numerous applications requiring robust long-range capabilities, essential for processing extensive inpu…
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…
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning
Zhongzhi Yu, Yang Zhang, Kaizhi Qian +2
Despite the impressive performance recently achieved by automatic speech recognition (ASR), we observe two primary challenges that hinder its broader applications: (1) The difficul…
NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants
Zhongzhi Yu, Yonggan Fu, Jiayi Yuan +2
Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things devices. However, it i…
Hint-Aug: Drawing Hints from Foundation Vision Transformers Towards Boosted Few-Shot Parameter-Efficient Tuning
Zhongzhi Yu, Shang Wu, Yonggan Fu +2
Despite the growing demand for tuning foundation vision transformers (FViTs) on downstream tasks, fully unleashing FViTs' potential under data-limited scenarios (e.g., few-shot tun…
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,…
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…
EyeCoD: Eye Tracking System Acceleration via FlatCam-based Algorithm & Accelerator Co-Design
Haoran You, Cheng Wan, Yang Zhao +11
Eye tracking has become an essential human-machine interaction modality for providing immersive experience in numerous virtual and augmented reality (VR/AR) applications desiring h…
LDP: Learnable Dynamic Precision for Efficient Deep Neural Network Training and Inference
Zhongzhi Yu, Yonggan Fu, Shang Wu +3
Low precision deep neural network (DNN) training is one of the most effective techniques for boosting DNNs' training efficiency, as it trims down the training cost from the finest…
MIA-Former: Efficient and Robust Vision Transformers via Multi-grained Input-Adaptation
Zhongzhi Yu, Yonggan Fu, Sicheng Li +2
ViTs are often too computationally expensive to be fitted onto real-world resource-constrained devices, due to (1) their quadratically increased complexity with the number of input…
Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System
Gang Yu, Zhongzhi Yu, Yemin Shi +7
Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clinics. The similarities among th…
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…
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…
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search
Yonggan Fu, Zhongzhi Yu, Yongan Zhang +1
AlphaGo's astonishing performance has ignited an explosive interest in developing deep reinforcement learning (DRL) for numerous real-world applications, such as intelligent roboti…
Kernel Quantization for Efficient Network Compression
Zhongzhi Yu, Yemin Shi, Tiejun Huang +1
This paper presents a novel network compression framework Kernel Quantization (KQ), targeting to efficiently convert any pre-trained full-precision convolutional neural network (CN…
Exploiting Partially Annotated Data for Temporal Relation Extraction
Qiang Ning, Zhongzhi Yu, Chuchu Fan +1
Annotating temporal relations (TempRel) between events described in natural language is known to be labor intensive, partly because the total number of TempRels is quadratic in the…