Benchmarking Neuromorphic Computing for Inference
Dunbar, L. Andrea
In the last decade, there has been significant progress in the IoT domain due to the advances in the accuracy of neural networks and the industrialization of efficient neural network accelerator ASICs. However, intelligent devices will need to be omnipresent to create a seamless consumer experience. To make this a reality, further progress is still needed in the low-power embedded machine learning domain. Neuromorphic computing is a technology suited to such low-power intelligent sensing. However, neuromorphic computing is hampered today by the fragmentation of the hardware providers and the difficulty of embedding and comparing the algorithms' performance. The lack of standard key performance indicators spanning across the hardware-software domains makes it difficult to benchmark different solutions for a given application on a fair basis. In this paper, we summarize the current benchmarking solutions used in both hardware and algorithms for neuromorphic systems, which are in general applicable to low-power systems. We then discuss the challenges in creating a fair and user-friendly method to benchmark such systems, before suggesting a clear methodology that includes possible key performance indicators.
Chapter 1, pp. 1-20, in "Industrial Artificial Intelligence Technologies and Applications", by River Publishers Series in Communications and Networking