MLUTNet: A Neural Network for Memory Based Reconfigurable Logic Device Architecture

Neural networks have been widely used and implemented on various hardware platforms, but high computational costs and low similarity of network structures relative to hardware structures are often obstacles to research. In this paper, we propose a novel neural network in combination with the structu...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 13; p. 6213
Main Authors Zang, Xuechen, Nakatake, Shigetoshi
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2021
Subjects
Accuracy
approximate computing
binary neural network
Computer applications
Computer architecture
Human performance
Logic
Machine learning
Memory
memory reconfigurable logic device
Neural networks
Programmable logic devices
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Summary:Neural networks have been widely used and implemented on various hardware platforms, but high computational costs and low similarity of network structures relative to hardware structures are often obstacles to research. In this paper, we propose a novel neural network in combination with the structural features of a recently proposed memory-based programmable logic device, compare it with the standard structure, and test it on common datasets with full and binary precision, respectively. The experimental results reveal that the new structured network can provide almost consistent full-precision performance and binary-precision performance ranging from 61.0% to 78.8% after using sparser connections and about 50% reduction in the size of the weight matrix.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11136213