Title
Activity-Based Resource Allocation for Motion Estimation Engines
Abbreviated Journal Title
J. Circuits Syst. Comput.
Keywords
Fault-handling by hardware reconfiguration; reconfigurable slack; diagnosis by comparison; hardware on-demand; video coding; self-healing; BLOCK-MATCHING ALGORITHM; ESTIMATION ARCHITECTURE; VLSI ARCHITECTURE; HARDWARE ARCHITECTURE; SEARCH ALGORITHM; RESILIENT; DESIGN; SCHEME; Computer Science, Hardware & Architecture; Engineering, Electrical &; Electronic
Abstract
An architecture proof-of-concept which adapts the throughput datapath based on the anticipation of computational demand in dynamic environments is demonstrated and evaluated for a motion estimation (ME) engine. The input signal characteristics are exploited to anticipate the time varying computational complexity as well as to instantiate dynamic replicas (DRs) to realize fault-resilience. The scheme employs amorphous processing elements (APEs) which either perform as active elements (AEs) to maintain quality/throughput, serve as DRs to increase reliability levels, or hibernate passively as reconfigurable slack (RS) available to other tasks. Experimental results from a hardware platform for field programmable gate array (FPGA)-based video encoding demonstrate power efficiency and fault-tolerance of the ME engine. A significant reduction in power consumption is achieved ranging from 83% for low-motion-activity scene's to 12.5% for high motion activity video scenes. The scenes motion activity is utilized to improve redundancy for the purpose of priority based diagnosis of the computing modules. In addition, a graceful degradation strategy is developed to recover from hard errors by adapting the search range of candidate motion vectors (MVs). This adaptive hardware scheme is shown to automatically demote the faulty resources in FPGA devices based on streaming performance.
Journal Title
Journal of Circuits Systems and Computers
Volume
24
Issue/Number
1
Publication Date
1-1-2015
Document Type
Article
Language
English
First Page
32
WOS Identifier
ISSN
0218-1266
Recommended Citation
"Activity-Based Resource Allocation for Motion Estimation Engines" (2015). Faculty Bibliography 2010s. 6590.
https://stars.library.ucf.edu/facultybib2010/6590
Comments
Authors: contact us about adding a copy of your work at STARS@ucf.edu