System-Level Design Space Exploration for High-Level Synthesis Under End-to-End Latency Constraints

被引:0
|
作者
Liao, Yuchao [1 ]
Adegbija, Tosiron [1 ]
Lysecky, Roman [1 ]
机构
[1] Univ Arizona, Dept Elect & Comp Engn, Tucson, AZ 85721 USA
基金
美国国家科学基金会;
关键词
Timing; Embedded systems; Space exploration; Optimization; Planning; Computational modeling; Hardware; Autonomous vehicles; System-level design; Estimation; Autonomous driving; high level synthesis; Pareto optimization; system-level design;
D O I
10.1109/TCAD.2024.3471892
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Many modern embedded systems have end-to-end (EtoE) latency constraints that necessitate precise timing to ensure high reliability and functional correctness. The combination of high-level synthesis (HLS) and design space exploration (DSE) enables the rapid generation of embedded systems using various constraints/directives to find Pareto-optimal configurations. Current HLS DSE approaches often address latency by focusing on individual components, without considering the EtoE latency during the system-level optimization process. However, to truly optimize the system under EtoE latency, we need a holistic approach that analyzes individual system components' timing constraints in the context of how the different components interact and impact the overall design. This article presents a novel system-level HLS DSE approach, called EtoE-DSE, that accommodates EtoE latency and variable timing constraints for complex multicomponent application-specific embedded systems. EtoE-DSE employs a latency estimation model and a pathfinding algorithm to identify and estimate the EtoE latency for paths between any endpoints. It also uses a frequency-based segmentation process to segment and prune the design space, alongside a latency-constrained optimization algorithm for efficiently and accurately exploring the system-level design space. We evaluate our approach using a real-world use case of an autonomous driving subsystem compared to the state-of-the-art in HLS DSE. We show that our approach yields substantially better-optimization results than prior DSE approaches, improving the quality of results by up to 89.26%, while efficiently identifying Pareto-optimal configurations in terms of energy and area.
引用
收藏
页码:1354 / 1365
页数:12
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