Authors: Naveed Imran; Rizwan A. Ashraf; Ronald F. DeMara
Addresses: Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816-2362, USA ' Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816-2362, USA ' Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816-2362, USA
Abstract: A self-aware signal processing architecture is proposed based on adaptive resource escalation which is guided by a multi-objective genetic algorithm (GA). The GA prioritises tasks within a reconfigurable hardware fabric to maintain the quality-of-service and power consumption objectives. Attainment of these objectives is subject to the intrinsic reliability and performance of the computational elements in the resource pool. A health metric at the application layer, such as peak-signal-to-noise ratio (PSNR) measurement in a discrete cosine transform (DCT) or measure of confidence in a support vector machine (SVM) classifier, is used to assess throughput performance. When performance decreases beyond acceptable tolerances, the primary objective is to maximally recover output quality. The secondary objective is to minimise power consumption which also depends upon the input signal characteristics, in addition to the utilised computational resources. An adaptive guidance function for GA-driven recovery is proposed and validated for these objectives. It retains healthy processing elements in the throughput data path to gracefully-degrade throughput by optimising resource selection.
Keywords: soft resilience; runtime multiobjective optimisation; evolvable hardware; FPGA devices; image processing; genetic algorithms; quality-aware; power-aware; support vector machines; SVM; discrete cosine transform; DCT; online reconfiguration; autonomous operation; self-aware signal processing; quality-of-service; QoS; power consumption; peak SNR; signal to noise ratio; PSNR; field-programmable gate arrays.
International Journal of Computational Vision and Robotics, 2015 Vol.5 No.1, pp.72 - 98
Received: 31 Jul 2013
Accepted: 05 Mar 2014
Published online: 24 Jan 2015 *