Formal Computation Models in Neuromorphic Computing: Challenges and Opportunities
Orlando Moreira
GrAI Matter Labs, NL
ABSTRACT
Neuromorphic computing is expected to enable ultra-low power solutions for inference-based computational functions, especially for real-time event-based systems. At the heart of the approach is the concept of Spiking Neural Networks (SNNs). SNNs can be thought of as a family of models of computation, each representing a different trade-off between computational efficiency and functional accuracy. In this talk, we will review SNN models and we will have a look at their fundamental analytical properties. We will discuss the challenge of selecting an adequate SNN model for hardware implementation, and the need for new approaches to analysis, functional composability, verification and testing. This is, at least partially, because in SNNs, contrary to popular concurrency models like Kahn Process Networks or Data Flow, event arrival times are intrinsic to the functional behavior of a graph.
