research
∙
09/15/2023
A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edge
Extreme edge platforms, such as in-vehicle smart devices, require effici...
share
research
∙
08/16/2023
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution
Nowadays, deep learning based methods have demonstrated impressive perfo...
research
∙
07/13/2023
PREFENDER: A Prefetching Defender against Cache Side Channel Attacks as A Pretender
Cache side channel attacks are increasingly alarming in modern processor...
research
∙
02/03/2023
PDPU: An Open-Source Posit Dot-Product Unit for Deep Learning Applications
Posit has been a promising alternative to the IEEE-754 floating point fo...
research
∙
11/16/2022
Fast and Accurate FSA System Using ELBERT: An Efficient and Lightweight BERT
As an application of Natural Language Processing (NLP) techniques, finan...
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...
research
∙
11/04/2022
An Efficient FPGA-based Accelerator for Deep Forest
Deep Forest is a prominent machine learning algorithm known for its high...
research
∙
10/28/2022
BEBERT: Efficient and robust binary ensemble BERT
Pre-trained BERT models have achieved impressive accuracy on natural lan...
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...
research
∙
10/14/2022
An Efficient FPGA Accelerator for Point Cloud
Deep learning-based point cloud processing plays an important role in va...
research
∙
08/12/2022
An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers
The Transformer has been an indispensable staple in deep learning. Howev...
research
∙
08/01/2022
GANDSE: Generative Adversarial Network based Design Space Exploration for Neural Network Accelerator Design
With the popularity of deep learning, the hardware implementation platfo...
research
∙
12/27/2021
Learning Robust and Lightweight Model through Separable Structured Transformations
With the proliferation of mobile devices and the Internet of Things, dee...
research
∙
10/12/2021
Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression
Existing deep convolutional neural networks (CNNs) generate massive inte...
research
∙
07/01/2021
Elbert: Fast Albert with Confidence-Window Based Early Exit
Despite the great success in Natural Language Processing (NLP) area, lar...
research
∙
09/18/2020
Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformer
Designing hardware accelerators for deep neural networks (DNNs) has been...
research
∙
09/06/2019
Training Deep Neural Networks Using Posit Number System
With the increasing size of Deep Neural Network (DNN) models, the high m...
research
∙
05/31/2019
Design Light-weight 3D Convolutional Networks for Video Recognition Temporal Residual, Fully Separable Block, and Fast Algorithm
Deep 3-dimensional (3D) Convolutional Network (ConvNet) has shown promis...
research
∙
05/08/2019
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding
The Connectionist Temporal Classification (CTC) has achieved great succe...
research
∙
12/12/2018
An Active-Passive Measurement Study of TCP Performance over LTE on High-speed Rails
High-speed rail (HSR) systems potentially provide a more efficient way o...
research
∙
07/04/2018
SGAD: Soft-Guided Adaptively-Dropped Neural Network
Deep neural networks (DNNs) have been proven to have many redundancies. ...
research
∙
07/10/2016
