Activity-Based Resource Allocation For Motion Estimation Engines

Keywords

diagnosis by comparison; Fault-handling by hardware reconfiguration; hardware on-demand; reconfigurable slack; self-healing; video coding

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.

Publication Date

1-1-2015

Publication Title

Journal of Circuits, Systems and Computers

Volume

24

Issue

1

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1142/S0218126615500048

Socpus ID

84928380204 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84928380204

This document is currently not available here.

Share

COinS