Optimized Event-Driven Spiking Neural Network for Low-Power Neuromorphic Platform
Postdoctoral Researcher, Western Sydney University – International Centre for Neuromorphic Systems (ICNS)
We present modular event-driven spiking neural architectures that use local synaptic and threshold adaptation rules to perform transformations between arbitrary spatio-temporal spike patterns. The architectures represent a highly abstracted model of existing Spiking Neural Network (SNN) architectures. We showcase Optimized Deep Event-driven Spiking neural network Architecture (ODESA) that can simultaneously learn hierarchical spatio-temporal features at multiple arbitrary time scales. ODESA performs online learning without the use of error back-propagation or the calculation of gradients. Using simple local adaptive selection thresholds at each node, the network rapidly learns to appropriately allocate its neuronal resources at each layer for any given problem without using an error measure. We also showcase an efficient hardware implementation of ODESA on FPGA. The computations performed by the architectures are event-driven and the entire communication between layers is binary event-based. We provide a potential blueprint for future neuromorphic architectures that can be run asynchronously and enable low-power always-on learning systems.