Comparative Evaluation between Accelerated RISC- V and ARM AI Inference Machines
Vasileios Christofas, Petros Amanatidis, Dimitris Karampatzakis, Θωμάς Λάγκας, Sotirios K. Goudos, Kostas E. Psannis, Panagiotis G. Sarigiannidis · 2023
Embedded AI development has been rapidly im-proving for the past few years and has had a great impact on edge AI networks. However, as neural networks become deeper and deeper it becomes more difficult to execute complicated tasks without sacrificing a good amount of power and performance. In this paper, we make a comparative evaluation between two AI acceleration devices. The first one features a RISC- V 64-bit processor while the other one is ARM powered. These devices are combined with AI co-processors, or ASICs, with computer vision capabilities. Our benchmark consists of a simple classification task split into multiple versions. The results showed that the RISC- V inference machine had 4 times lower consumption while the ARM machine was up to 15 times faster in our largest network. We discuss the results in great detail while keeping our focus on all aspects equally. Finally, we make recommendations based on their usage and application.