Search Results for author: Xiaofan Zhang

Found 23 papers, 12 papers with code

AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models

no code implementations21 Jan 2022 Xiaofan Zhang, Zongwei Zhou, Deming Chen, Yu Emma Wang

By evaluating on SQuAD, a model found by AutoDistill achieves an 88. 4% F1 score with 22. 8M parameters, which reduces parameters by more than 62% while maintaining higher accuracy than DistillBERT, TinyBERT, and NAS-BERT.

Knowledge Distillation Model Compression +1

EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search

1 code implementation24 Nov 2021 Qian Jiang, Xiaofan Zhang, Deming Chen, Minh N. Do, Raymond A. Yeh

In this work, we propose End-to-end Hardware-aware DNAS (EH-DNAS), a seamless integration of end-to-end hardware benchmarking, and fully automated DNAS to deliver hardware-efficient deep neural networks on various platforms, including Edge GPUs, Edge TPUs, Mobile CPUs, and customized accelerators.

Neural Architecture Search

One-shot Weakly-Supervised Segmentation in Medical Images

1 code implementation21 Nov 2021 Wenhui Lei, Qi Su, Ran Gu, Na Wang, Xinglong Liu, Guotai Wang, Xiaofan Zhang, Shaoting Zhang

Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.

Denoising Medical Image Segmentation +2

F-CAD: A Framework to Explore Hardware Accelerators for Codec Avatar Decoding

no code implementations8 Mar 2021 Xiaofan Zhang, Dawei Wang, Pierce Chuang, Shugao Ma, Deming Chen, Yuecheng Li

Creating virtual avatars with realistic rendering is one of the most essential and challenging tasks to provide highly immersive virtual reality (VR) experiences.

Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search Space

1 code implementation1 Jan 2021 Yuhong Li, Cong Hao, Xiaofan Zhang, JinJun Xiong, Wen-mei Hwu, Deming Chen

This raises the question of whether we can find an effective proxy search space (PS) that is only a small subset of GS to dramatically improve RandomNAS’s search efficiency while at the same time keeping a good correlation for the top-performing architectures.

Image Classification Neural Architecture Search

Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices

no code implementations14 Oct 2020 Cong Hao, Yao Chen, Xiaofan Zhang, Yuhong Li, JinJun Xiong, Wen-mei Hwu, Deming Chen

High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators.

EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions

no code implementations6 May 2020 Yuhong Li, Cong Hao, Xiaofan Zhang, Xinheng Liu, Yao Chen, JinJun Xiong, Wen-mei Hwu, Deming Chen

We formulate the co-search problem by fusing DNN search variables and hardware implementation variables into one solution space, and maximize both algorithm accuracy and hardware implementation quality.

Neural Architecture Search

HybridDNN: A Framework for High-Performance Hybrid DNN Accelerator Design and Implementation

no code implementations8 Apr 2020 Hanchen Ye, Xiaofan Zhang, Zhize Huang, Gengsheng Chen, Deming Chen

To speedup Deep Neural Networks (DNN) accelerator design and enable effective implementation, we propose HybridDNN, a framework for building high-performance hybrid DNN accelerators and delivering FPGA-based hardware implementations.

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

1 code implementation6 Jan 2020 Pengfei Xu, Xiaofan Zhang, Cong Hao, Yang Zhao, Yongan Zhang, Yue Wang, Chaojian Li, Zetong Guan, Deming Chen, Yingyan Lin

Specifically, AutoDNNchip consists of two integrated enablers: (1) a Chip Predictor, built on top of a graph-based accelerator representation, which can accurately and efficiently predict a DNN accelerator's energy, throughput, and area based on the DNN model parameters, hardware configuration, technology-based IPs, and platform constraints; and (2) a Chip Builder, which can automatically explore the design space of DNN chips (including IP selection, block configuration, resource balancing, etc.

SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection

1 code implementation25 Jun 2019 Xiaofan Zhang, Cong Hao, Haoming Lu, Jiachen Li, Yuhong Li, Yuchen Fan, Kyle Rupnow, JinJun Xiong, Thomas Huang, Honghui Shi, Wen-mei Hwu, Deming Chen

Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy.

Object Detection

A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices

2 code implementations20 May 2019 Xiaofan Zhang, Cong Hao, Yuhong Li, Yao Chen, JinJun Xiong, Wen-mei Hwu, Deming Chen

Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as DNN model inference accuracy, and quality of service (QoS), such as inference latency, throughput, and power consumption.

Object Detection

FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge

2 code implementations9 Apr 2019 Cong Hao, Xiaofan Zhang, Yuhong Li, Sitao Huang, JinJun Xiong, Kyle Rupnow, Wen-mei Hwu, Deming Chen

While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA devices also makes it challenging for DNN deployment.

Object Detection

Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering

1 code implementation CVPR 2019 Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, Heng Huang

In this paper, we propose a novel end-to-end trainable Video Question Answering (VideoQA) framework with three major components: 1) a new heterogeneous memory which can effectively learn global context information from appearance and motion features; 2) a redesigned question memory which helps understand the complex semantics of question and highlights queried subjects; and 3) a new multimodal fusion layer which performs multi-step reasoning by attending to relevant visual and textual hints with self-updated attention.

Question Answering Video Question Answering +1

SiamVGG: Visual Tracking using Deeper Siamese Networks

4 code implementations7 Feb 2019 Yuhong Li, Xiaofan Zhang

It combines a Convolutional Neural Network (CNN) backbone and a cross-correlation operator, and takes advantage of the features from exemplary images for more accurate object tracking.

Visual Object Tracking Visual Tracking

Design Flow of Accelerating Hybrid Extremely Low Bit-width Neural Network in Embedded FPGA

no code implementations31 Jul 2018 Junsong Wang, Qiuwen Lou, Xiaofan Zhang, Chao Zhu, Yonghua Lin, Deming Chen

To create such accelerators, we propose a design flow for accelerating the extremely low bit-width neural network (ELB-NN) in embedded FPGAs with hybrid quantization schemes.

Edge-computing Quantization

Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs

no code implementations23 Mar 2018 Chuanhao Zhuge, Xinheng Liu, Xiaofan Zhang, Sudeep Gummadi, JinJun Xiong, Deming Chen

Deep Convolutional Neural Networks have become a Swiss knife in solving critical artificial intelligence tasks.

Face Recognition

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

10 code implementations CVPR 2018 Yuhong Li, Xiaofan Zhang, Deming Chen

We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance.

Crowd Counting Scene Recognition

Embedding Label Structures for Fine-Grained Feature Representation

no code implementations CVPR 2016 Xiaofan Zhang, Feng Zhou, Yuanqing Lin, Shaoting Zhang

However, previous studies have rarely focused on learning a fined-grained and structured feature representation that is able to locate similar images at different levels of relevance, e. g., discovering cars from the same make or the same model, both of which require high precision.

Fine-Grained Image Classification General Classification +2

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