Abstract
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.