AutoSoC：面向空中机器人的自动化SoC协同设计
AutoSoC: Automating Algorithm-SOC Co-design for Aerial Robots
摘要：
空中自主机器（无人机）具有大量有前途的应用和用例。虽然这些自主机器的普及程度不断增长，但仍存在许多挑战，例如耐力和敏捷性，这些挑战可能会阻碍这些机器的实际部署。闭环控制频率必须高，以实现高灵活性。然而，由于空中机器人资源有限的性质，实现高控制回路频率是非常具有挑战性的，需要仔细的算法和机载计算机的协同设计。这样的努力需要连接不同领域的基础设施，即机器人技术、机器学习和系统架构设计。为此，我们提出了AutoSoC，这是一个用于协同设计算法的框架，也是一个用于端到端基于学习的航空自主机器的硬件加速器系统。我们通过训练空中机器人在密集杂乱的环境中导航的避障算法来证明该框架的有效性。对于性能最佳的算法，我们的框架生成各种加速器设计候选人具有不同的性能，面积和功耗。该框架还运行ASIC的布局和布线流程，并生成可用于流片最终硬件芯片的平面加速器的布局。
英文摘要：
Aerial autonomous machines (Drones) has a plethora of promising applications and use cases. While the popularity of these autonomous machines continues to grow, there are many challenges, such as endurance and agility, that could hinder the practical deployment of these machines. The closed-loop control frequency must be high to achieve high agility. However, given the resource-constrained nature of the aerial robot, achieving high control loop frequency is hugely challenging and requires careful co-design of algorithm and onboard computer. Such an effort requires infrastructures that bridge various domains, namely robotics, machine learning, and system architecture design. To that end, we present AutoSoC, a framework for co-designing algorithms as well as hardware accelerator systems for end-to-end learning-based aerial autonomous machines. We demonstrate the efficacy of the framework by training an obstacle avoidance algorithm for aerial robots to navigate in a densely cluttered environment. For the best performing algorithm, our framework generates various accelerator design candidates with varying performance, area, and power consumption. The framework also runs the ASIC flow of place and route and generates a layout of the floor-planed accelerator, which can be used to tape-out the final hardware chip.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
