Prof. Franz Franchetti Profile

Prof. Franz Franchetti

at Carnegie Mellon Univ

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Publications (2)

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Proceedings Article | 20 June 2014 Paper

High assurance SPIRAL

Franz Franchetti, Aliaksei Sandryhaila, Jeremy Johnson

Proceedings Volume 9091, 90911O (2014) https://doi.org/10.1117/12.2053974

KEYWORDS: Sensors, Control systems, Computing systems, Matrices, Systems modeling, Embedded systems, Silicon, Detection and tracking algorithms, Signal processing, Sensor fusion

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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.

Proceedings Article | 29 April 2009 Paper

High-performance synthetic aperture radar image formation on commodity multicore architectures

Daniel McFarlin, Franz Franchetti, Markus Püschel, José M. Moura

Proceedings Volume 7337, 733708 (2009) https://doi.org/10.1117/12.818399

KEYWORDS: Synthetic aperture radar, Image acquisition, Image processing, Genetic algorithms, Optimization (mathematics), Commercial off the shelf technology, Radar, Reflectivity, Fourier transforms, Computing systems

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Synthetic Aperture Radar (SAR) image processing platforms have to process increasingly large datasets under and hard real-time deadlines. Upgrading these platforms is expensive. An attractive solution to this problem is to couple high performance, general-purpose Commercial-Off-The-Shelf (COTS) architectures such as IBM's Cell BE and Intel's Core with software implementations of SAR algorithms. While this approach provides great flexibility, achieving the requisite performance is difficult and time-consuming. The reason is the highly parallel nature and general complexity of modern COTS microarchitectures. To achieve the best performance, developers have to interweave of various complex optimizations including multithreading, the use of SIMD vector extensions, and careful tuning to the memory hierarchy. In this paper, we demonstrate the computer generation of high performance code for SAR implementations on Intel's multicore platforms based on the Spiral framework and system. The key is to express SAR and its building blocks in Spiral's formal domain-specific language to enable automatic vectorization, parallelization, and memory hierarchy tuning through rewriting at a high abstraction level and automatic exploration of choices. We show that Spiral produces code for the latest Intel quadcore platforms that surpasses competing hand-tuned implementations on the Cell Blade, an architecture with twice as many cores and three times the memory bandwidth. Specifically, we show an average performance of 39 Gigaflops/sec for 16-Megapixel and 100-Megapixel SAR images with runtimes of 0.56 and 3.76 seconds respectively.

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