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High assurance SPIRAL
Author(s): Franz Franchetti; Aliaksei Sandryhaila; Jeremy R. Johnson
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Paper Abstract

In this paper we introduce High Assurance SPIRAL to solve the last mile problem for the synthesis of high assurance implementations of controllers for vehicular systems that are executed in today’s and future embedded and high performance embedded system processors. High Assurance SPIRAL is a scalable methodology to translate a high level specification of a high assurance controller into a highly resource-efficient, platform-adapted, verified control software implementation for a given platform in a language like C or C++. High Assurance SPIRAL proves that the implementation is equivalent to the specification written in the control engineer’s domain language. Our approach scales to problems involving floating-point calculations and provides highly optimized synthesized code. It is possible to estimate the available headroom to enable assurance/performance trade-offs under real-time constraints, and enables the synthesis of multiple implementation variants to make attacks harder. At the core of High Assurance SPIRAL is the Hybrid Control Operator Language (HCOL) that leverages advanced mathematical constructs expressing the controller specification to provide high quality translation capabilities. Combined with a verified/certified compiler, High Assurance SPIRAL provides a comprehensive complete solution to the efficient synthesis of verifiable high assurance controllers. We demonstrate High Assurance SPIRALs capability by co-synthesizing proofs and implementations for attack detection and sensor spoofing algorithms and deploy the code as ROS nodes on the Landshark unmanned ground vehicle and on a Synthetic Car in a real-time simulator.

Paper Details

Date Published: 20 June 2014
PDF: 7 pages
Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90911O (20 June 2014); doi: 10.1117/12.2053974

Published in SPIE Proceedings Vol. 9091:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII
Ivan Kadar, Editor(s)

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