Activity-Based Resource Allocation for Motion Estimation Engines

    Authors

    N. Imran; R. A. Ashraf; J. Lee;R. F. DeMara

    Comments

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    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

    WOS:000350769900005

    ISSN

    0218-1266

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