Benchmarking methods that account for fundamental architectural differences of neuromorphic computing

Develop benchmarking methodologies that rigorously account for the fundamental architectural differences of neuromorphic computing relative to conventional and GPU-based systems, including a principled decomposition of energy and performance costs across neuron dynamics, spike generation, synaptic operations, and spike communication.

Background

The authors call for empirical validation of their theoretical framework but note that existing neuromorphic benchmarks are largely application-driven and do not capture architectural differences. The distinct cost structure of neuromorphic systems—especially communication and event-driven computation—complicates apples-to-apples comparisons.

A comprehensive, architecture-aware benchmarking methodology is needed to evaluate neuromorphic systems fairly and to guide algorithm design, yet how to construct such benchmarks remains unresolved.