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Download fileA Cluster of FPAAs to Recognize Images using Neural Networks
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posted on 24.05.2021, 18:00 by Daniel García MorenoDaniel García Moreno, Alberto A Del Barrio, Guillermo Botella, Jennifer HaslerAnalog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper 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 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network.
Funding
ConvergenciaA Big dAta-Hpc: de Los sensores a las Aplicaciones (S2018/TCS-4423)
Detección temprana de desarrollo tumoral mediante computación de altas prestaciones (PR26/16-20B-1)
cHIMERA: Heterogeneity and specialization in the Post-Moore ERA (RTI2018-093684-BI00)
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daniel10@ucm.esORCID of Submitting Author
0000-0003-1707-106XSubmitting Author's Institution
Complutense University of MadridSubmitting Author's Country
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in IEEE Transactions on Circuits and Systems II: Express Briefs
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