Design Space Exploration of Embedded SoC Architectures for Real-Time Optimal Control

Abstract: Empowering resource-limited robots to execute computationally intensive tasks such as locomotion and manipulation is challenging. This project provides a comprehensive design space exploration to determine optimal hardware computation architectures suitable for model-based control algorithms. We profile and optimize representative architectural designs across general-purpose scalar, vector processors, and specialized accelerators. Specifically, we compare CPUs, vector machines, and domain-specialized accelerators with kernel-level benchmarks and end-to-end representative robotic workloads. Our exploration provides a quantitative performance, area, and utilization comparison and analyzes the trade-offs between these representative distinct architectural designs. We demonstrate that architectural modifications, software, and system optimization can alleviate bottlenecks and enhance utilization. Finally, we propose a code generation flow to simplify the engineering work for mapping robotic workloads to specialized architectures.

• The paper introduces a systematic design space exploration methodology that optimizes SoC architectures for real-time optimal control.
• It employs a profiling framework and integrates hardware-software co-design with the Gemmini accelerator to boost computational throughput.
• Benchmarking results demonstrate notable performance improvements, validating enhanced resource utilization and practical feasibility in real-time applications.

Design Space Exploration of Embedded SoC Architectures for Real-Time Optimal Control

The paper "Design Space Exploration of Embedded SoC Architectures for Real-Time Optimal Control" by Kris Shengjun Dong et al. presents an in-depth evaluation and development of System-on-Chip (SoC) architectures designed to enhance the efficiency of real-time optimal control tasks. The authors address a significant challenge in the field of embedded systems: optimizing the performance of SoCs for demanding real-time applications.

Methodology and Contributions

The authors employ a systematic design space exploration methodology to evaluate various configurations of embedded SoC architectures. They focus on integrating efficient hardware and software co-design strategies tailored for optimal control problems. A noteworthy aspect of their approach includes leveraging Gemmini, a configurable matrix multiplication accelerator, to enhance computational throughput.

Key contributions include:

• Profiling and Optimization: The paper outlines a profiling framework to identify performance bottlenecks. This framework aids in directing optimization efforts effectively.
• Hardware-Software Co-Design: The study emphasizes the integration of hardware accelerators with software optimizations to achieve near-optimal performance on embedded platforms.
• Tools and Repositories: The introduction of novel features and infrastructures in existing repositories, such as Accelerated-TinyMPC and matlib, stems from their extensive benchmarking process.

Numerical Results and Findings

The evaluation section offers substantial numerical data supporting the efficacy of their optimized SoC configurations. The authors present performance improvements over baseline configurations, highlighting substantial gains in computational efficiency and real-time processing capabilities.

• Performance Gains: The implementations showcase significant accelerations in specific control algorithms, validating the proposed design enhancements.
• Resource Utilization: Efficient utilization of hardware resources, including the Gemmini GEMV component, is documented, emphasizing the practical feasibility of their approaches.

Implications and Future Directions

The implications of this research are twofold:

1. Practical Applications: The findings provide a path for developing more capable and efficient embedded systems, particularly in areas requiring rapid, real-time decision-making such as autonomous vehicles and industrial automation.
2. Theoretical Advancements: The results contribute to the broader understanding of hardware-software interaction in optimal control scenarios, potentially guiding future design methodologies.

The work's outcomes suggest that further exploration into advanced compiler optimizations and heterogeneous computing environments could yield additional performance benefits. Additionally, ongoing advancements in AI can potentially integrate with these optimized platforms, further broadening their applicability.

In conclusion, this paper presents a comprehensive exploration into the design of embedded SoC architectures, offering substantial contributions to both the academic and practical landscape of real-time optimal control. As AI and control systems continue to evolve, the methodologies and findings detailed in this work provide a solid foundation for future research and development in this dynamic field.