Search Results for author: Chao Fang

Found 14 papers, 1 papers with code

BETA: Binarized Energy-Efficient Transformer Accelerator at the Edge

no code implementations22 Jan 2024 Yuhao Ji, Chao Fang, Zhongfeng Wang

Existing binary Transformers are promising in edge deployment due to their compact model size, low computational complexity, and considerable inference accuracy.

Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design

no code implementations22 Sep 2023 Chao Fang, Wei Sun, Aojun Zhou, Zhongfeng Wang

At the algorithm level, a bidirectional weight pruning method, dubbed BDWP, is proposed to leverage the N:M sparsity of weights during both forward and backward passes of DNN training, which can significantly reduce the computational cost while maintaining model accuracy.

Computational Efficiency Scheduling

A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edge

no code implementations15 Sep 2023 Longwei Huang, Chao Fang, Qiong Li, Jun Lin, Zhongfeng Wang

However, many edge devices struggle to boost inference throughput of various quantized DNNs due to the varying quantization levels, and these devices lack floating-point (FP) support for on-device learning, which prevents them from improving model accuracy while ensuring data privacy.


Caching-at-STARS: the Next Generation Edge Caching

no code implementations1 Aug 2023 Zhaoming Hu, Ruikang Zhong, Chao Fang, Yuanwei Liu

As long-term decision processes, the optimization problems based on independent and coupled phase-shift models of Caching-at-STARS contain both continuous and discrete decision variables, and are suitable for solving with deep reinforcement learning (DRL) algorithm.

BEBERT: Efficient and Robust Binary Ensemble BERT

1 code implementation28 Oct 2022 Jiayi Tian, Chao Fang, Haonan Wang, Zhongfeng Wang

Pre-trained BERT models have achieved impressive accuracy on natural language processing (NLP) tasks.

Binarization Computational Efficiency +1

An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers

no code implementations12 Aug 2022 Chao Fang, Aojun Zhou, Zhongfeng Wang

(1) From algorithm perspective, we propose a sparsity inheritance mechanism along with an inherited dynamic pruning (IDP) method to obtain a series of N:M sparse candidate Transformers rapidly.

Computational Efficiency Model Compression

Joint Scheduling and Throughput Maximization in Self-backhauled Millimeter Wave Cellular Networks

no code implementations4 Jun 2021 Chao Fang, Charitha Madapatha, Behrooz Makki, Tommy Svensson

Integrated access and backhaul (IAB) networks have the potential to provide high data rate in both access and backhaul networks by sharing the same spectrum.


PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving

no code implementations3 Aug 2020 Kuan-Hui Lee, Matthew Kliemann, Adrien Gaidon, Jie Li, Chao Fang, Sudeep Pillai, Wolfram Burgard

In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning.

Autonomous Driving

Hybrid Precoding in Cooperative Millimeter Wave Networks

no code implementations13 Jan 2020 Chao Fang, Behrooz Makki, Jingya Li, Tommy Svensson

Considering joint transmissions and BS silence strategy, we propose hybrid precoding algorithms which minimize the sum power consumption of the base stations (BSs), for both fully- and partially-connected hybrid precoding (FHP and PHP, respectively) schemes, for single-carrier and orthogonal frequency-division multiplexing systems.

Real-Time Panoptic Segmentation from Dense Detections

no code implementations CVPR 2020 Rui Hou, Jie Li, Arjun Bhargava, Allan Raventos, Vitor Guizilini, Chao Fang, Jerome Lynch, Adrien Gaidon

Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution.

Clustering object-detection +4

Training Deep Neural Networks Using Posit Number System

no code implementations6 Sep 2019 Jinming Lu, Siyuan Lu, Zhisheng Wang, Chao Fang, Jun Lin, Zhongfeng Wang, Li Du

With the increasing size of Deep Neural Network (DNN) models, the high memory space requirements and computational complexity have become an obstacle for efficient DNN implementations.

Image Classification

MUFold-SS: Protein Secondary Structure Prediction Using Deep Inception-Inside-Inception Networks

no code implementations12 Sep 2017 Chao Fang, Yi Shang, Dong Xu

Results: Here, a very deep neural network, the deep inception-inside-inception networks (Deep3I), is proposed for protein secondary structure prediction and a software tool was implemented using this network.

Image Classification Protein Secondary Structure Prediction

Cannot find the paper you are looking for? You can Submit a new open access paper.