48 citations · 208 across the 32 of their papers we have counts for
33 papers
HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing
Haoran You, Yotam Nitzan, Lingzhi Zhang +8
Creative image editing tools, such as Adobe Photoshop's Remove and Generative Fill buttons, are central to everyday customer use and account for a major share of traffic. However,…
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…
Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training
Lexington Whalen, Zhenbang Du, Haoran You +4
Training diffusion models (DMs) requires substantial computational resources due to multiple forward and backward passes across numerous timesteps, motivating research into efficie…
Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture
Zishen Wan, Che-Kai Liu, Hanchen Yang +13
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, l…
When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models
Haoran You, Yichao Fu, Zheng Wang +2
Autoregressive Large Language Models (LLMs) have achieved impressive performance in language tasks but face two significant bottlenecks: (1) quadratic complexity in the attention m…
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…
Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI
Zishen Wan, Che-Kai Liu, Hanchen Yang +7
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, have significantly impacted various aspects of our lives. However, the curren…
NetDistiller: Empowering Tiny Deep Learning via In-Situ Distillation
Shunyao Zhang, Yonggan Fu, Shang Wu +4
Boosting the task accuracy of tiny neural networks (TNNs) has become a fundamental challenge for enabling the deployments of TNNs on edge devices which are constrained by strict li…
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…
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)…
Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design
Yonggan Fu, Zhifan Ye, Jiayi Yuan +4
Novel view synthesis is an essential functionality for enabling immersive experiences in various Augmented- and Virtual-Reality (AR/VR) applications, for which generalizable Neural…
Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at Vision Transformer Inference
Haoran You, Yunyang Xiong, Xiaoliang Dai +5
Vision Transformers (ViTs) have shown impressive performance but still require a high computation cost as compared to convolutional neural networks (CNNs), one reason is that ViTs'…
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…
SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning
Haoran You, Baopu Li, Zhanyi Sun +2
Neural architecture search (NAS) has demonstrated amazing success in searching for efficient deep neural networks (DNNs) from a given supernet. In parallel, the lottery ticket hypo…
i-FlatCam: A 253 FPS, 91.49 J/Frame Ultra-Compact Intelligent Lensless Camera for Real-Time and Efficient Eye Tracking in VR/AR
Yang Zhao, Ziyun Li, Yonggan Fu +9
We present a first-of-its-kind ultra-compact intelligent camera system, dubbed i-FlatCam, including a lensless camera with a computational (Comp.) chip. It highlights (1) a predict…
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…
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…
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…
I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization
Tong Geng, Chunshu Wu, Yongan Zhang +6
Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three years. Compared with other deep learning modalities, high-performance hardware acceleration of…
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design
Haoran You, Tong Geng, Yongan Zhang +2
Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning model. However, it can be notoriously challenging to inference GCNs over large graph dataset…
G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency
Yongan Zhang, Haoran You, Yonggan Fu +3
Graph Neural Networks (GNNs) have emerged as the state-of-the-art (SOTA) method for graph-based learning tasks. However, it still remains prohibitively challenging to inference GNN…
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…