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

TL;DR

This paper introduces EtoE-DSE, a system-level HLS design space exploration method that considers end-to-end latency constraints, improving optimization of complex embedded systems by up to 89.26%.

Contribution

It presents a novel holistic approach for system-level HLS DSE that incorporates EtoE latency analysis and optimization, unlike prior component-focused methods.

Findings

Achieves up to 89.26% improvement in optimization quality.

Effectively estimates EtoE latency using a pathfinding algorithm.

Efficiently explores design space with latency-constrained optimization.

Abstract

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 paper presents a novel system-level HLS DSE approach, called EtoE-DSE, that accommodates EtoE latency and variable timing constraints for complex multi-component 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.