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Design Space Exploration of Heterogeneous-Accelerator SoCs with Hyperparameter Optimization

Thanh Cong 1 François Charot 1
1 TARAN - Architectures matérielles spécialisées pour l’ère post loi-de-Moore
Inria Rennes – Bretagne Atlantique , IRISA-D3 - ARCHITECTURE
Abstract : Modern SoC systems consist of general-purpose processor cores augmented with large numbers of specialized accelerators. Building such systems requires a design flow allowing the design space to be explored at the system level with an appropriate strategy. In this paper, we describe a methodology allowing to explore the design space of power-performance heterogeneous SoCs by combining an architecture simulator (gem5-Aladdin) and a hyperparameter optimization method (Hyperopt). This methodology allows different types of parallelism with loop unrolling strategies and memory coherency interfaces to be swept. The flow has been applied to a convolutional neural network algorithm. We show that the most energy efficient architecture achieves a 2x to 4x improvement in energy-delay-product compared to an architecture without parallelism. Furthermore, the obtained solution is more efficient than commonly implemented architectures (Systolic, 2D-mapping, and Tiling). We also applied the methodology to find the optimal architecture including its coherency interface for a complex SoC made up of six accelerated-workloads.We show that a hybrid interface appears to be the most efficient; it reaches 22% and 12% improvement in energy-delay-product compared to just only using non-coherent and only LLC-coherent models, respectively.
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Conference papers
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https://hal.inria.fr/hal-03119732
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Submitted on : Tuesday, January 26, 2021 - 1:59:41 PM
Last modification on : Friday, January 21, 2022 - 3:12:16 AM
Long-term archiving on: : Tuesday, April 27, 2021 - 6:12:03 PM

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  • HAL Id : hal-03119732, version 1

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Thanh Cong, François Charot. Design Space Exploration of Heterogeneous-Accelerator SoCs with Hyperparameter Optimization. ASP-DAC 2021 - 26th Asia and South Pacific Design Automation Conference, Jan 2021, Virtual Conference, Japan. pp.1-6. ⟨hal-03119732⟩

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