4.3 Article

FARSI: An Early-stage Design Space Exploration Framework to Tame the Domain-specific System-on-chip Complexity

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ASSOC COMPUTING MACHINERY
DOI: 10.1145/3544016

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Domain specific systems; design space exploration; simulation; augmented reality

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Domain-specific SoCs (DSSoCs) are a solution for domains with strict power, performance, and area requirements. However, they suffer from complex systems and a difficult design space. To address this, a design space exploration framework called FARSI is proposed, which includes a fast simulator, efficient exploration heuristic, and co-design capabilities.
Domain-specific SoCs (DSSoCs) are an attractive solution for domains with extremely stringent power, performance, and area constraints. However, DSSoCs suffer from two fundamental complexities. On the one hand, their many specialized hardware blocks result in complex systems and thus high development effort. On the other hand, their many system knobs expand the complexity of design space, making the search for the optimal design difficult. Thus to reach prevalence, taming such complexities is necessary. To address these challenges, in this work, we identify the necessary features of an early-stage design space exploration framework that targets the complex design space of DSSoCs and provide an instance of one such framework that we refer to as FARSI. FARSI provides an agile system-level simulator with speed up and accuracy of 8,400x and 98.5% compared to Synopsys Platform Architect. FARSI also provides an efficient exploration heuristic and achieves up to 62x and 35x improvement in convergence time compared to the classic simulated annealing (SA) and modern Multi-Objective Optimistic Search. This is done by augmenting SA with architectural reasoning such as locality exploitation and bottleneck relaxation. Furthermore, we embed various co-design capabilities and show that, on average, they have a 32% impact on the convergence rate. Finally, we demonstrate that using development-cost-aware policies can lower the system complexity, both in terms of the component count and variation by as much as 60% and 82% (e.g., for Network-on-a-Chip subsystem), respectively.

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