Schematic Generation of Programmable Analog Neural Networks for Signal Proccessing

This paper presents a methodology for generating analog neural networks (ANNs) from trained TensorFlow models using an intermediate description of the network. All needed circuitry and helper circuits are available in a block library provided for the operation in an energy efficient low power region. The weights of the ANN are programmable by the corresponding bitstream, which is also generated by the proposed approach. The feasibility of the approach is demonstrated with two examples, the larger being an ANN for arrhythmia detection of electrocardiograms (ECGs) with 2,570 neurons and 10,042 weights.