Abstract

The desire to create novel computing systems, paired with recent advances in neuroscientific understanding of the brain, has led researchers to develop neuromorphic architectures that emulate the brain. To date, such models are developed, trained, and deployed on the same substrate. However, excessive co-dependence between the substrate and the algorithm prevents portability, or at the very least requires reconstructing and retraining the model whenever the substrate changes. This paper proposes a well-defined abstraction layer – the Neuromorphic instruction set architecture, or NISA – that separates a neural application’s algorithmic specification from the underlying execution substrate, and describes the Aivo 1 framework, which demonstrates the concrete advantages of such an abstraction layer. Aivo consists of a NISA implementation for a rate-encoded neuromorphic system based on the cortical column

Citations