Biologically-inspired massively-parallel computing

Abstract

Half a century of progress in computer technology has delivered machines of formidable capability and an expectation that similar advances will continue into the foreseeable future. However, much of the past progress has been driven by developments in semiconductor technology following Moore's Law, and there are strong grounds for believing that these cannot continue at the same rate. This, and related issues, suggest that there are huge challenges ahead in meeting the expectations of future progress, such as understanding how to exploit massive parallelism and how to deliver improvements in energy efficiency and reliability in the face of diminishing component reliability. Alongside these issues, recent advances in machine learning have created a demand for machines with cognitive capabilities, for example, to control autonomous vehicles, that we will struggle to deliver. Biological systems have, through evolution, found solutions to many of these problems, but we lack a fundamental understanding of how these solutions function. If we could advance our understanding of biological systems, we would open a rich source of ideas for unblocking progress in our engineered systems. An overview is given of SpiNNaker - a spiking neural network architecture. The SpiNNaker machine puts these principles together in the form of a massively parallel computer architecture designed both to model the biological brain, in order to accelerate our understanding of its principles of operation, and also to explore engineering applications of such machines.