Spiking Neural Networks on FPGAs...

This research investigates a high-performance architecture for spiking neural networks that optimizes data precision and streaming of configuration data stored in main memory. The neural network is based on the Izhikevich model and mapped to a CPU-FPGA hybrid device using a high-level synthesis flow. The active area of the network is configurable and this feature is used to create an energy proportional system.