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Lee, Jongeun
Renew: Reconfigurable and Neuromorphic Computing Lab
Research Interests
  • Reconfigurable processor architecture, neuromorphic processor, stochastic computing

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PICA: Processor Idle Cycle Aggregation for Energy-Efficient Embedded Systems

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Title
PICA: Processor Idle Cycle Aggregation for Energy-Efficient Embedded Systems
Author
Lee, JongeunShrivastava, Aviral
Keywords
Embedded application; Energy efficient; Energy reduction; Hardware extension; Idle cycles; Low Power; Low-power mode; Nested Loops; Optimal parameter; Parameter-tuning; Simulation-based optimizations; Steady-state analysis; Three dimensions
Issue Date
201207
Publisher
ASSOC COMPUTING MACHINERY
Citation
ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, v.11, no.2, pp.1 - 27
Abstract
Processor Idle Cycle Aggregation (PICA) is a promising approach for low-power execution of processors, in which small memory stalls are aggregated to create large ones, enabling profitable switch of the processor into low-power mode. We extend the previous approach in three dimensions. First we develop static analysis for the PICA technique and present optimal parameters for five common types of loops based on steady-state analysis. Second, to remedy the weakness of software-only control in varying environment, we enhance PICA with minimal hardware extension that ensures correct execution for any loops and parameters, thus greatly facilitating exploration-based parameter tuning. Third, we demonstrate that our PICA technique can be applied to certain types of nested loops with variable bounds, thus enhancing the applicability of PICA. We validate our analytical model against simulation-based optimization and also show, through our experiments on embedded application benchmarks, that our technique can be applied to a wide range of loops with average 20% energy reductions, compared to executions without PICA.
URI
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DOI
http://dx.doi.org/10.1145/2220336.2220338
ISSN
1539-9087
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