An FPGA-Based Accelerator for Analog VLSI Artificial Neural Network Emulation

Abstract

Analog VLSI circuits are being used successfully to implement Artificial Neural Networks (ANNs). These analog circuits exhibit nonlinear transfer function characteristics and suffer from device mismatches, degrading network performance. Because of the high cost involved with analog VLSI production, it is beneficial to predict implementation performance during design. We present an FPGA-based accelerator for the emulation of large (500+ synapses, 10k+ test samples) single-neuron ANNs implemented in analog VLSI. We used hardware time-multiplexing to scale network size and maximize hardware usage. An on-chip CPU controls the data flow through various memory systems to allow for large test sequences. We show that Block-RAM availability is the main implementation bottleneck and that a trade-off arises between emulation speed and hardware resources. However, we can emulate large amounts of synapses on an FPGA with limited resources. We have obtained a speedup of 30.5 times with respect to an optimized software implementation on a desktop computer.