Award Winner 2025
Bridging Real-Time Robotics and Computer Architecture
Press Release AnnouncementAbstract
Robotics technology, poised to play a pivotal role in shaping future societal frameworks, is projected to reach a deployment of 20 million units and a market valuation of US$70 billion by the end of this decade. Despite its growing significance, there remains a pronounced gap between the fields of robotics and computer architecture research. This thesis endeavors to bridge this gap, contributing to the advancement of real-time robotic systems through two primary initiatives: (i) the development of comprehensive, open-source benchmark suites coupled with systematic performance studies, and (ii) the devising of efficient computer architectures for robotics.
Firstly, the thesis introduces extensive benchmark suites for robotics tasks and applications, meticulously developed from scratch to address the critical need for performance-focused, comprehensive benchmarking environments in systems and hardware research. These suites are accompanied by thorough performance analyses, delving into the computational behaviors and architectural implications of the applications. Employing advanced optimization techniques such as compile-time calculations, data prefetching, and manual code vectorization, the benchmarks offer insights into execution efficiency and the effective utilization of hardware resources. Moreover, the benchmarks are designed to be simulator-friendly, ensuring compatibility with architectural simulators, which is crucial for the predominantly simulator-based methodologies used in hardware research. Such suites and performance studies are instrumental in deepening the understanding and improving the synergy between robotics and computer architecture, facilitating the development of more efficient and effective robotic systems. Through this approach, the thesis contributes significantly to the field by enhancing the benchmarking tools available for evaluating and optimizing robotic applications within computer architectures.
Secondly, the thesis proposes efficient computer architectures tailored for robotics, focusing on the development of application-specific hardware accelerators and domain-specific processors. These hardware accelerators are specifically designed to optimize crucial robotic kernels, such as collision detection, enhancing their efficiency significantly. Conversely, the domain-specific processors, while not exclusively tailored for individual kernels, are optimized to deliver enhanced performance across a broader spectrum of robotic applications. The process begins with extensive profiling of robotic applications to identify performance bottlenecks. This is followed by the design of targeted architectural solutions aimed at accelerating these key operations, thereby improving the overall execution times of the systems. The empirical results demonstrate significant speedup potentials, achieving up to 41.4 times with application-specific hardware accelerators and 3.8 times with domain-specific processors.
Collectively, these contributions not only propel the performance capabilities of real-time robotic systems but also establish a groundwork for ongoing and future research at the critical nexus of robotics and computer architecture.