Fast Classification Using Sparsely Active Spiking Networks

Abstract: Spike generation and routing is typically the most energy-demanding operation in neuromorphic hardware built using spiking neurons. Spiking neural networks running on neuromorphic hardware, however, often use rate-coding where the neuron’s spike rate is treated as the information-carrying quantity. Rate-coding is a highly inefficient coding scheme with minimal information content in each spike, which requires the transmission of a large number of spikes. We describe an alternative type of spiking networks based on temporal coding where neuron spiking activity is very sparse and information is encoded in the time of each spike. We implemented the proposed networks on an FPGA platform and we use these sparsely active spiking networks to classify MNIST digits. The network FPGA implementation produces the classification output using only few tens of spikes from the hidden layer, and the classification result is obtained very quickly, typically within 1-3 synaptic time constants. We describe the idealized network dynamics and how these dynamics are adapted to allow an efficient implementation on digital hardware. Our results illustrate the importance of making use of the temporal dynamics in spiking networks in order to maximize the information content of each spike, which ultimately leads to reduced
spike counts, improved energy efficiency, and faster response times.