Spiking Neural Network Project

Spiking Neural Network Project

Abstract— Artificial neural networks (ANNs) are reasonably well served by today’s von Neumann CPU architectures and GPU variants, especially when assisted by coprocessors optimized for streaming matrix arithmetic. Spiking neural network (SNN) models, on the other hand, are exceedingly poorly served by conventional architectures. Just as the value of ANNs was not fully appreciated until the advent of sufficiently fast CPUs and GPUs, the same could be the case for spiking models— except different computing architectures will be required. The neuromorphic-computing field of research spans a range of different neuron models and levels of abstraction. Loihi (pronounced “low-EE-hee”) is motivated by a particular class of algorithmic results and perspectives from our survey of computational neuroscience and recent neuromorphic advances. We approach the field with an eye for mathematical rigor, top-down modeling, rapid architecture iteration, and quantitative benchmarking. Our aim is to develop algorithms and hardware in a principled way as much as possible. We begin this paper with our definition of the SNN computational model and the features that motivated Loihi’s architectural requirements. We then describe the architecture that supports those requirements and provide an overview of the chip’s asynchronous design implementation. We conclude with some preliminary 14-nm silicon results. Importantly, we present a result that unambiguously demonstrates the value of spike-based computation for one foundational problem. We view this as a significant result in light of ongoing debate about the value of spikes as a computational tool in both mainstream and neuromorphic communities. The skepticism towards spikes is well founded, but, in our research, we have moved on from this question, given the existence of an example that potentially generalizes to a very broad class of neural networks, namely all recurrent networks.