b'Yanzhi Wang'

research

∙ 06/01/2023

SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds

Text-to-image diffusion models can create stunning images from natural l...

0 Yanyu Li, et al. ∙

research

∙ 04/30/2023

DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning

Rehearsal-based approaches are a mainstay of continual learning (CL). Th...

0 Zifeng Wang, et al. ∙

research

∙ 03/13/2023

Can Adversarial Examples Be Parsed to Reveal Victim Model Information?

Numerous adversarial attack methods have been developed to generate impe...

8 Yuguang Yao, et al. ∙

research

∙ 03/05/2023

DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network

The rapid advances in Vision Transformer (ViT) refresh the state-of-the-...

0 Xuan Shen, et al. ∙

research

∙ 12/15/2022

Rethinking Vision Transformers for MobileNet Size and Speed

With the success of Vision Transformers (ViTs) in computer vision tasks,...

0 Yanyu Li, et al. ∙

research

∙ 12/09/2022

All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management

During the deployment of deep neural networks (DNNs) on edge devices, ma...

0 Yifan Gong, et al. ∙

research

∙ 11/22/2022

Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors

As data become increasingly vital for deep learning, a company would be ...

0 Sizhe Chen, et al. ∙

research

∙ 11/21/2022

You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model

Large-scale Transformer models bring significant improvements for variou...

5 Shengkun Tang, et al. ∙

research

∙ 11/15/2022

HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers

While vision transformers (ViTs) have continuously achieved new mileston...

0 Peiyan Dong, et al. ∙

research

∙ 11/02/2022

The Lottery Ticket Hypothesis for Vision Transformers

The conventional lottery ticket hypothesis (LTH) claims that there exist...

0 Xuan Shen, et al. ∙

research

∙ 10/09/2022

Pruning Adversarially Robust Neural Networks without Adversarial Examples

Adversarial pruning compresses models while preserving robustness. Curre...

17 Tong Jian, et al. ∙

research

∙ 10/08/2022

Advancing Model Pruning via Bi-level Optimization

The deployment constraints in practical applications necessitate the pru...

9 Yihua Zhang, et al. ∙

research

∙ 10/06/2022

DeltaFS: Pursuing Zero Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Devices

Data compression has been widely adopted to release mobile devices from ...

0 Chao Wu, et al. ∙

research

∙ 09/22/2022

Layer Freezing Data Sieving: Missing Pieces of a Generic Framework for Sparse Training

Recently, sparse training has emerged as a promising paradigm for effici...

0 Geng Yuan, et al. ∙

research

∙ 09/18/2022

PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems

Processing-in-memory (PIM), an increasingly studied neuromorphic hardwar...

0 Qing Jin, et al. ∙

research

∙ 09/18/2022

StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks

Obstacle detection is a safety-critical problem in robot navigation, whe...

22 Hongyu Li, et al. ∙

research

∙ 09/12/2022

Understanding Time Variations of DNN Inference in Autonomous Driving

Deep neural networks (DNNs) are widely used in autonomous driving due to...

0 Liangkai Liu, et al. ∙

research

∙ 07/04/2022

Quantum Neural Network Compression

Model compression, such as pruning and quantization, has been widely app...

0 Zhirui Hu, et al. ∙

research

∙ 06/21/2022

CoCoPIE XGen: A Full-Stack AI-Oriented Optimizing Framework

There is a growing demand for shifting the delivery of AI capability fro...

4 Xiaofeng Li, et al. ∙

research

∙ 06/04/2022

Student-AI Creative Writing: Pedagogical Strategies for Applying Natural Language Generation in Schools

AI natural language generation (NLG) is a process where computer systems...

0 David James Woo, et al. ∙

research

∙ 06/02/2022

Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization

Neural architecture search (NAS) and network pruning are widely studied ...

0 Yanyu Li, et al. ∙

research

∙ 06/02/2022

EfficientFormer: Vision Transformers at MobileNet Speed

Vision Transformers (ViT) have shown rapid progress in computer vision t...

31 Yanyu Li, et al. ∙

research

∙ 05/12/2022

Neural Network-based OFDM Receiver for Resource Constrained IoT Devices

Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are us...

0 Nasim Soltani, et al. ∙

research

∙ 04/09/2022

Deep neural network goes lighter: A case study of deep compression techniques on automatic RF modulation recognition for Beyond 5G networks

Automatic RF modulation recognition is a primary signal intelligence (SI...

0 Anu Jagannath, et al. ∙

research

∙ 02/10/2022

F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization

Neural network quantization is a promising compression technique to redu...

0 Qing Jin, et al. ∙

research

∙ 02/09/2022

Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets

The lottery ticket hypothesis (LTH) has shown that dense models contain ...

3 Tianlong Chen, et al. ∙

research

∙ 12/21/2021

Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting

Deep neural networks (DNNs) have shown to provide superb performance in ...

0 Minghai Qin, et al. ∙

research

∙ 10/30/2021

ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA

This work targets the commonly used FPGA (field-programmable gate array)...

0 Sung-En Chang, et al. ∙

research

∙ 10/30/2021

RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions

This work proposes a novel Deep Neural Network (DNN) quantization framew...

1 Sung-En Chang, et al. ∙

research

∙ 10/27/2021

ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers

Adversarial patch attacks that craft the pixels in a confined region of ...

0 Husheng Han, et al. ∙

research

∙ 09/08/2021

Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks

Quantization has been proven to be a vital method for improving the infe...

10 Cheng Gong, et al. ∙

research

∙ 07/06/2021

CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inference

A compact, accurate, and bitwidth-programmable in-memory computing (IMC)...

5 Zhiyu Chen, et al. ∙

research

∙ 07/01/2021

Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?

There have been long-standing controversies and inconsistencies over the...

2 Xiaolong Ma, et al. ∙

research

∙ 06/16/2021

Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI

Recent research demonstrated the promise of using resistive random acces...

0 Geng Yuan, et al. ∙

research

∙ 06/16/2021

FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator

Recent works demonstrated the promise of using resistive random access m...

4 Geng Yuan, et al. ∙

research

∙ 06/15/2021

Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression

Compressing Deep Neural Network (DNN) models to alleviate the storage an...

0 Sheng Lin, et al. ∙

research

∙ 03/05/2021

Teachers Do More Than Teach: Compressing Image-to-Image Models

Generative Adversarial Networks (GANs) have achieved huge success in gen...

0 Qing Jin, et al. ∙

research

∙ 02/19/2021

Lottery Ticket Implies Accuracy Degradation, Is It a Desirable Phenomenon?

In deep model compression, the recent finding "Lottery Ticket Hypothesis...

8 Ning Liu, et al. ∙

research

∙ 12/13/2020

Learn-Prune-Share for Lifelong Learning

In lifelong learning, we wish to maintain and update a model (e.g., a ne...

0 Zifeng Wang, et al. ∙

research

∙ 12/08/2020

Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework

Deep Neural Networks (DNNs) have achieved extraordinary performance in v...

6 Sung-En Chang, et al. ∙

research

∙ 11/04/2020

DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search

The convolutional neural network has achieved great success in fulfillin...

1 Yushuo Guan, et al. ∙

research

∙ 09/27/2020

Simultaneous Relevance and Diversity: A New Recommendation Inference Approach

Relevance and diversity are both important to the success of recommender...

0 Yifang Liu, et al. ∙

research

∙ 09/16/2020

MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework

With the tremendous success of deep learning, there exists imminent need...

0 Sung-En Chang, et al. ∙

research

∙ 09/03/2020

ESMFL: Efficient and Secure Models for Federated Learning

Deep Neural Networks are widely applied to various domains. The successf...

0 Sheng Lin, et al. ∙

research

∙ 08/14/2020

AntiDote: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency

Convolutional Neural Networks (CNNs) achieved great cognitive performanc...

17 Fuxun Yu, et al. ∙

research

∙ 08/09/2020

One for Many: Transfer Learning for Building HVAC Control

The design of building heating, ventilation, and air conditioning (HVAC)...

28 Shichao Xu, et al. ∙

research

∙ 07/20/2020

Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices

Mobile devices are becoming an important carrier for deep learning tasks...

20 Wei Niu, et al. ∙

research

∙ 05/11/2020

CSB-RNN: A Faster-than-Realtime RNN Acceleration Framework with Compressed Structured Blocks

Recurrent neural networks (RNNs) have been widely adopted in temporal se...

0 Runbin Shi, et al. ∙

research

∙ 04/22/2020

Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization

High-end mobile platforms rapidly serve as primary computing devices for...

0 Wei Niu, et al. ∙

research

∙ 04/12/2020

A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods

To address the large model size and intensive computation requirement of...

13 Tianyun Zhang, et al. ∙

Yanzhi Wang

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