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09/19/2023
GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
The remarkable capabilities and intricate nature of Artificial Intellige...
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06/23/2023
NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants
Tiny deep learning has attracted increasing attention driven by the subs...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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, ...
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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 ...
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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...
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05/17/2022
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
Neural networks (NNs) with intensive multiplications (e.g., convolutions...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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11/19/2021
FBNetV5: Neural Architecture Search for Multiple Tasks in One Run
Neural Architecture Search (NAS) has been widely adopted to design accur...
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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.,...
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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...
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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)...
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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...
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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...
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07/16/2021
DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference
Semantic segmentation for scene understanding is nowadays widely demande...
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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...
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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...
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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)...
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03/19/2021
HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark
HardWare-aware Neural Architecture Search (HW-NAS) has recently gained t...
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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...
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01/25/2021
CPT: Efficient Deep Neural Network Training via Cyclic Precision
Low-precision deep neural network (DNN) training has gained tremendous a...
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01/07/2021
Max-Affine Spline Insights Into Deep Network Pruning
In this paper, we study the importance of pruning in Deep Networks (DNs)...
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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...
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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...
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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...
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10/28/2020
DNA: Differentiable Network-Accelerator Co-Search
Powerful yet complex deep neural networks (DNNs) have fueled a booming d...
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10/24/2020
ShiftAddNet: A Hardware-Inspired Deep Network
Multiplication (e.g., convolution) is arguably a cornerstone of modern d...
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06/15/2020
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
The compression of Generative Adversarial Networks (GANs) has lately dra...
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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...
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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...
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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...
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03/02/2020