Related papers: Machine Learning-Based Automated Design Space Exploration for Autonomous Aerial Robots

Machine Learning-Based Automated Design Space Exploration for Autonomous Aerial Robots

URL: http://arxiv.org/abs/2102.02988v1
Date: Fri, 5 Feb 2021 03:50:54 GMT
Title: Machine Learning-Based Automated Design Space Exploration for Autonomous Aerial Robots
Authors: Srivatsan Krishnan, Zishen Wan, Kshitij Bharadwaj, Paul Whatmough, Aleksandra Faust, Sabrina Neuman, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi
Abstract summary: Building domain-specific architectures for autonomous aerial robots is challenging due to a lack of systematic methodology for designing onboard compute. We introduce a novel performance model called the F-1 roofline to help architects understand how to build a balanced computing system. To navigate the cyber-physical design space automatically, we subsequently introduce AutoPilot.
Score: 55.056709056795206
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Building domain-specific architectures for autonomous aerial robots is challenging due to a lack of systematic methodology for designing onboard compute. We introduce a novel performance model called the F-1 roofline to help architects understand how to build a balanced computing system for autonomous aerial robots considering both its cyber (sensor rate, compute performance) and physical components (body-dynamics) that affect the performance of the machine. We use F-1 to characterize commonly used learning-based autonomy algorithms with onboard platforms to demonstrate the need for cyber-physical co-design. To navigate the cyber-physical design space automatically, we subsequently introduce AutoPilot. This push-button framework automates the co-design of cyber-physical components for aerial robots from a high-level specification guided by the F-1 model. AutoPilot uses Bayesian optimization to automatically co-design the autonomy algorithm and hardware accelerator while considering various cyber-physical parameters to generate an optimal design under different task level complexities for different robots and sensor framerates. As a result, designs generated by AutoPilot, on average, lower mission time up to 2x over baseline approaches, conserving battery energy.

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