Spiking Neural Networks (SNNs) utilize an event-based representation to perform more efficient computation than existing artificial neural networks. SNNs show a lot of promise for low energy computation, but are still limited by the lack of quality training tools and efficient hardware implementations.

Our recent work published at the ACM/IEEE International Symposium of Field-Programmable Gate Arrays (ISFPGA) extends the Xilinx FINN architecture to support streaming spiking neural networks (S2N2). S2N2 efficiently supports both axonal and synaptic delays for feedforward networks with interlayer connections. We show that because of the spikes’ binary nature, a binary tensor can be used for addressing the input events of a layer. We show that S2N2 works well for automatic modulation classification — an important problem for modern wireless networks.

The work was done in collaboration with Xilinx. For more details, check out Ali’s talk at ISFPGA

Paper Reference: Alireza Khodamoradi, Kristof Denolf, and Ryan Kastner, “S2N2: A FPGA Accelerator for Streaming Spiking Neural Network“, International Symposium on Field-Programmable Gate Arrays (ISFPGA) (pdf)