A Cluster of FPAAs to Recognize Images Using Neural Networks

被引：9

作者：

Garcia Moreno, Daniel ^{[1
]}

Del Barrio, Alberto A. ^{[1
]}

Botella, Guillermo ^{[1
]}

Hasler, Jennifer ^{[2
]}

机构：

[1] Univ Complutense Madrid, Dept Comp Architecture & Automat, Madrid 28040, Spain

[2] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2021年 / 68卷 / 11期

关键词：

Field programmable analog arrays; Artificial neural networks; Neurons; Discrete cosine transforms; Adders; Cams; Wires; FPAA; cluster; neural network; analog computing; approximate computing; classification;

D O I：

10.1109/TCSII.2021.3077392

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Analog computing has been recovering its relevance in the recent years. Field-Programmable Analog Arrays (FPAAs) are the equivalent to Field-Programmable Gate Arrays (FPGAs) but in the analog and mixed-signal domain. In order to increase the amount of analog resources, in this brief a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28 x 28 images from MNIST. Results show that the analog network is able to obtain similar results as the software baseline network.

引用

页码：3391 / 3395

页数：5

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