b'Yingyan Lin'

research

∙ 05/09/2023

Instant-NeRF: Instant On-Device Neural Radiance Field Training via Algorithm-Accelerator Co-Designed Near-Memory Processing

Instant on-device Neural Radiance Fields (NeRFs) are in growing demand f...

0 fcq, et al. ∙

research

∙ 04/25/2023

Hint-Aug: Drawing Hints from Foundation Vision Transformers Towards Boosted Few-Shot Parameter-Efficient Tuning

Despite the growing demand for tuning foundation vision transformers (FV...

0 Zhongzhi Yu, et al. ∙

research

∙ 04/24/2023

Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design

Novel view synthesis is an essential functionality for enabling immersiv...

0 Yonggan Fu, et al. ∙

research

∙ 04/24/2023

Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence

Real-time and robust photorealistic avatars for telepresence in AR/VR ha...

0 Yonggan Fu, et al. ∙

research

∙ 03/19/2023

ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement

Social ambiance describes the context in which social interactions happe...

0 Chaojian Li, et al. ∙

research

∙ 12/05/2022

INGeo: Accelerating Instant Neural Scene Reconstruction with Noisy Geometry Priors

We present a method that accelerates reconstruction of 3D scenes and obj...

4 Chaojian Li, et al. ∙

research

∙ 12/02/2022

RT-NeRF: Real-Time On-Device Neural Radiance Fields Towards Immersive AR/VR Rendering

Neural Radiance Field (NeRF) based rendering has attracted growing atten...

0 Chaojian Li, et al. ∙

research

∙ 11/18/2022

Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention During Vision Transformer Inference

Vision Transformers (ViTs) have shown impressive performance but still r...

0 Haoran You, et al. ∙

research

∙ 11/09/2022

ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention

Vision Transformer (ViT) has emerged as a competitive alternative to con...

0 Jyotikrishna Dass, et al. ∙

research

∙ 10/24/2022

NASA: Neural Architecture Search and Acceleration for Hardware Inspired Hybrid Networks

Multiplication is arguably the most cost-dominant operation in modern de...

0 Huihong Shi, et al. ∙

research

∙ 10/18/2022

ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design

Vision Transformers (ViTs) have achieved state-of-the-art performance on...

0 Haoran You, et al. ∙

research

∙ 07/24/2022

e-G2C: A 0.14-to-8.31 μJ/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM

This work presents the first silicon-validated dedicated EGM-to-ECG (G2C...

0 fcq, et al. ∙

research

∙ 07/08/2022

SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning

Neural architecture search (NAS) has demonstrated amazing success in sea...

1 Haoran You, et al. ∙

research

∙ 06/15/2022

i-FlatCam: A 253 FPS, 91.49 μJ/Frame Ultra-Compact Intelligent Lensless Camera for Real-Time and Efficient Eye Tracking in VR/AR

We present a first-of-its-kind ultra-compact intelligent camera system, ...

0 fcq, et al. ∙

research

∙ 06/02/2022

EyeCoD: Eye Tracking System Acceleration via FlatCam-based Algorithm Accelerator Co-Design

Eye tracking has become an essential human-machine interaction modality ...

0 Haoran You, et al. ∙

research

∙ 06/02/2022

DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks

Efficient deep neural network (DNN) models equipped with compact operato...

10 Yonggan Fu, et al. ∙

research

∙ 05/17/2022

ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks

Neural networks (NNs) with intensive multiplications (e.g., convolutions...

8 Haoran You, et al. ∙

research

∙ 03/21/2022

BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art...

20 Cheng Wan, et al. ∙

research

∙ 03/20/2022

PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication

Graph Convolutional Networks (GCNs) is the state-of-the-art method for l...

7 Cheng Wan, et al. ∙

research

∙ 03/16/2022

Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?

Vision transformers (ViTs) have recently set off a new wave in neural ar...

5 Yonggan Fu, et al. ∙

research

∙ 03/15/2022

LDP: Learnable Dynamic Precision for Efficient Deep Neural Network Training and Inference

Low precision deep neural network (DNN) training is one of the most effe...

12 Zhongzhi Yu, et al. ∙

research

∙ 03/07/2022

I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization

Graph Convolutional Networks (GCNs) have drawn tremendous attention in t...

52 Tong Geng, et al. ∙

research

∙ 12/22/2021

GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art...

10 Haoran You, et al. ∙

research

∙ 12/21/2021

MIA-Former: Efficient and Robust Vision Transformers via Multi-grained Input-Adaptation

ViTs are often too computationally expensive to be fitted onto real-worl...

15 Zhongzhi Yu, et al. ∙

research

∙ 11/19/2021

FBNetV5: Neural Architecture Search for Multiple Tasks in One Run

Neural Architecture Search (NAS) has been widely adopted to design accur...

10 Bichen Wu, et al. ∙

research

∙ 11/04/2021

RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms

There exists a gap in terms of the signals provided by pacemakers (i.e.,...

0 Yongan Zhang, et al. ∙

research

∙ 10/26/2021

Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks

Deep Neural Networks (DNNs) are known to be vulnerable to adversarial at...

20 Yonggan Fu, et al. ∙

research

∙ 09/18/2021

G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency

Graph Neural Networks (GNNs) have emerged as the state-of-the-art (SOTA)...

0 Yongan Zhang, et al. ∙

research

∙ 09/11/2021

2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency

The recent breakthroughs of deep neural networks (DNNs) and the advent o...

0 Yonggan Fu, et al. ∙

research

∙ 08/17/2021

O-HAS: Optical Hardware Accelerator Search for Boosting Both Acceleration Performance and Development Speed

The recent breakthroughs and prohibitive complexities of Deep Neural Net...

3 Mengquan Li, et al. ∙

research

∙ 07/16/2021

DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference

Semantic segmentation for scene understanding is nowadays widely demande...

0 Chaojian Li, et al. ∙

research

∙ 06/11/2021

A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning

Driven by the explosive interest in applying deep reinforcement learning...

0 Yonggan Fu, et al. ∙

research

∙ 06/11/2021

Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators

While maximizing deep neural networks' (DNNs') acceleration efficiency r...

7 Yonggan Fu, et al. ∙

research

∙ 04/22/2021

InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks

The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT)...

0 Yonggan Fu, et al. ∙

research

∙ 03/19/2021

HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark

HardWare-aware Neural Architecture Search (HW-NAS) has recently gained t...

0 Chaojian Li, et al. ∙

research

∙ 03/01/2021

GEBT: Drawing Early-Bird Tickets in Graph Convolutional Network Training

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art...

0 Haoran You, et al. ∙

research

∙ 01/25/2021

CPT: Efficient Deep Neural Network Training via Cyclic Precision

Low-precision deep neural network (DNN) training has gained tremendous a...

0 Yonggan Fu, et al. ∙

research

∙ 01/07/2021

Max-Affine Spline Insights Into Deep Network Pruning

In this paper, we study the importance of pruning in Deep Networks (DNs)...

0 Randall Balestriero, et al. ∙

research

∙ 01/04/2021

SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training

The record-breaking performance of deep neural networks (DNNs) comes wit...

5 Xiaohan Chen, et al. ∙

research

∙ 12/24/2020

FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremen...

0 Yonggan Fu, et al. ∙

research

∙ 12/24/2020

Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search

AlphaGo's astonishing performance has ignited an explosive interest in d...

0 Yonggan Fu, et al. ∙

research

∙ 10/28/2020

DNA: Differentiable Network-Accelerator Co-Search

Powerful yet complex deep neural networks (DNNs) have fueled a booming d...

0 Yongan Zhang, et al. ∙

research

∙ 10/24/2020

ShiftAddNet: A Hardware-Inspired Deep Network

Multiplication (e.g., convolution) is arguably a cornerstone of modern d...

0 Haoran You, et al. ∙

research

∙ 06/15/2020

AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks

The compression of Generative Adversarial Networks (GANs) has lately dra...

42 Yonggan Fu, et al. ∙

research

∙ 06/08/2020

AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles

Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a tremen...

0 Sicong Liu, et al. ∙

research

∙ 05/07/2020

SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation

We present SmartExchange, an algorithm-hardware co-design framework to t...

6 fcq, et al. ∙

research

∙ 05/03/2020

TIMELY: Pushing Data Movements and Interfaces in PIM Accelerators Towards Local and in Time Domain

Resistive-random-access-memory (ReRAM) based processing-in-memory (R^2PI...

0 Weitao Li, et al. ∙

research

∙ 03/02/2020

A New MRAM-based Process In-Memory Accelerator for Efficient Neural Network Training with Floating Point Precision

The excellent performance of modern deep neural networks (DNNs) comes at...

0 Hongjie Wang, et al. ∙

research

∙ 02/26/2020

DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architectures

The recent breakthroughs in deep neural networks (DNNs) have spurred a t...

0 fcq, et al. ∙

research

∙ 01/06/2020

AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs

Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a growin...

0 Pengfei Xu, et al. ∙

Yingyan Lin

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