A Generative AI for Heterogeneous Network-on-Chip Design Space Pruning

Abstract

Often suffering from under-optimization, Networkson-Chip (NoCs) heavily impact the efficiency of domain-specific Systems-on-Chip. To cope with this issue, heterogeneous NoCs are promising alternatives. Nevertheless, the design of optimized NoCs satisfying multiple performance objectives is extremely challenging and requires significant expertise. Prior works failed to combine many objectives or required an extended design space exploration time. In this paper, we propose an approach based on generative artificial intelligence to help pruning complex design spaces for heterogeneous NoCs, according to configurable performance objectives. This is made possible by the ability of Generative Adversarial Networks to learn and generate relevant design candidates for the target NoCs. The speed and flexibility of our solution enable a fast generation of optimized NoCs that fit users' expectations. Through some experiments, we show how to obtain competitive NoC designs reducing the power consumption with no communication performance or area penalty compared to a given conventional NoC design.

An Overview on Generative AI at Scale With Edge–Cloud Computing

“…Power consumption is a major concern in cloud computing [135,83]. The centralized computation infrastructure consumes a significant amount of electricity in running user requests as well as training large models.…”