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A Cluster of FPAAs to Recognize Images using Neural Networks
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  • Daniel García Moreno ,
  • Alberto A Del Barrio ,
  • Guillermo Botella ,
  • Jennifer Hasler
Daniel García Moreno
Complutense University of Madrid, Complutense University of Madrid

Corresponding Author:[email protected]

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Alberto A Del Barrio
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Guillermo Botella
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Jennifer Hasler
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Analog 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.
Nov 2021Published in IEEE Transactions on Circuits and Systems II: Express Briefs volume 68 issue 11 on pages 3391-3395. 10.1109/TCSII.2021.3077392