2024/10/29 by Emmanuel Irabor, Musavi, Mariam, Irabor, Emmanuel +5 · 2 citations
Physics and Astronomy · Engineering · #Particle Detector Development and Performance #CCD and CMOS Imaging Sensors #Radiation Effects in Electronics
paper · pdf · doi:10.48550/arxiv.2410.22262
Next-generation artificial intelligence (AI) workloads are posing challenges of scalability and robustness in terms of execution time due to their intrinsic evolving data-intensive characteristics. In this paper, we aim to analyse the potential bottlenecks caused due to data movement characteristics of AI workloads on scale-out accelerator architectures composed of multiple chiplets. Our methodology captures the unicast and multicast communication traffic of a set of AI workloads and assesses aspects such as the time spent in such communications and the amount of multicast messages as a function of the number of employed chiplets. Our studies reveal that some AI workloads are potentially vulnerable to the dominant effects of communication, especially multicast traffic, which can become a performance bottleneck and limit their scalability. Workload profiling insights suggest to architect a flexible interconnect solution at chiplet level in order to improve the performance, efficiency and scalability of next-generation AI accelerators.