You are here

UCF Digital Collections ยป ETD Collection

Exploring FPGA Implementation for Binarized Neural Network Inference

Download pdf | Full Screen View

Date Issued:
2018
Abstract/Description:
Deep convolutional neural network has taken an important role in machine learning algorithm. It is widely used in different areas such as computer vision, robotics, and biology. However, the models of deep neural networks become larger and more computation complexity which is a big obstacle for such huge model to implement on embedded systems. Recent works have shown the binarized neural networks (BNN), utilizing binarized (i.e. +1 and -1) convolution kernel and binarized activation function, can significantly reduce the parameter size and computation cost, which makes it hardware-friendly for Field-Programmable Gate Arrays (FPGAs) implementation with efficient energy cost. This thesis proposes to implement a new parallel convolutional binarized neural network (i.e. PC-BNN) on FPGA with accurate inference. The embedded PC-BNN is designed for image classification on CIFAR-10 dataset and explores the hardware architecture and optimization of customized CNN topology.The parallel-convolution binarized neural network has two parallel binarized convolution layers which replaces the original single binarized convolution layer. It achieves around 86% on CIFAR-10 dataset and owns 2.3Mb parameter size. We implement our PC-BNN inference into the Xilinx PYNQ Z1 FPGA board which only has 4.9Mb on-chip Block RAM. Since the ultra-small network parameter, the whole model parameters can be stored on on-chip memory which can greatly reduce energy consumption and computation latency. Meanwhile, we design a new pipeline streaming architecture for PC-BNN hardware inference which can further increase the performance. The experiment results show that our PC-BNN inference on FPGA achieves 930 frames per second and 387.5 FPS/Watt, which are among the best throughput and energy efficiency compared to most recent works.
Title: Exploring FPGA Implementation for Binarized Neural Network Inference.
37 views
16 downloads
Name(s): Yang, Li, Author
Fan, Deliang, Committee Chair
Zhang, Wei, Committee Member
Lin, Mingjie, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2018
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Deep convolutional neural network has taken an important role in machine learning algorithm. It is widely used in different areas such as computer vision, robotics, and biology. However, the models of deep neural networks become larger and more computation complexity which is a big obstacle for such huge model to implement on embedded systems. Recent works have shown the binarized neural networks (BNN), utilizing binarized (i.e. +1 and -1) convolution kernel and binarized activation function, can significantly reduce the parameter size and computation cost, which makes it hardware-friendly for Field-Programmable Gate Arrays (FPGAs) implementation with efficient energy cost. This thesis proposes to implement a new parallel convolutional binarized neural network (i.e. PC-BNN) on FPGA with accurate inference. The embedded PC-BNN is designed for image classification on CIFAR-10 dataset and explores the hardware architecture and optimization of customized CNN topology.The parallel-convolution binarized neural network has two parallel binarized convolution layers which replaces the original single binarized convolution layer. It achieves around 86% on CIFAR-10 dataset and owns 2.3Mb parameter size. We implement our PC-BNN inference into the Xilinx PYNQ Z1 FPGA board which only has 4.9Mb on-chip Block RAM. Since the ultra-small network parameter, the whole model parameters can be stored on on-chip memory which can greatly reduce energy consumption and computation latency. Meanwhile, we design a new pipeline streaming architecture for PC-BNN hardware inference which can further increase the performance. The experiment results show that our PC-BNN inference on FPGA achieves 930 frames per second and 387.5 FPS/Watt, which are among the best throughput and energy efficiency compared to most recent works.
Identifier: CFE0007384 (IID), ucf:52067 (fedora)
Note(s): 2018-12-01
M.S.E.E.
Engineering and Computer Science, Electrical Engineering and Computer Engineering
Masters
This record was generated from author submitted information.
Subject(s): FPGA -- deep learning -- inference
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007384
Restrictions on Access: public 2018-12-15
Host Institution: UCF

In Collections