Fast approximation for peak power driven voltage partitioning in almost linear time

Voltage partitioning on functional units/blocks targeting peak power minimization has been demonstrated to be effective for energy reduction considering voltage island shutdown impact. However, the existing technique can only solve this NP-hard problem efficiently on small designs. In this paper, a...

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Bibliographic Details
Published in:2012 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) pp. 698 - 704
Main Authors: Wang, Jia, Chen, Xiaodao, Liu, Lin, Hu, Shiyan
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 05.11.2012
IEEE
Series:ACM Conferences
Subjects:
Applied computing > Arts and humanities > Architecture (buildings) > Computer-aided design
Applied computing > Physical sciences and engineering > Engineering > Computer-aided design
Approximation methods
Capacitance
Dynamic programming
Energy Efficiency
Greedy algorithms
Hardware > Hardware validation
Heuristic algorithms
Linear programming
Linear Time Approximation Scheme
Mathematics of computing > Mathematical analysis > Functional analysis > Approximation
Partitioning algorithms
Peak Power
Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis
Voltage Partitioning
linear time approximation scheme
peak power
energy efficiency
voltage partitioning
ISBN:9781450315739, 1450315739
ISSN:1092-3152
Online Access:Get full text
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Summary:Voltage partitioning on functional units/blocks targeting peak power minimization has been demonstrated to be effective for energy reduction considering voltage island shutdown impact. However, the existing technique can only solve this NP-hard problem efficiently on small designs. In this paper, a much faster linear time approximation scheme is proposed, which can approximate the optimal voltage partitioning solution within a factor 1 + ε, for any 0 < ε < 1, and runs in O(n + 1/εO(1)) time, where n is the number of functional units. There are multiple ingredients in such a surprisingly low time complexity algorithm. It first categorizes all the functional units into big functional units and small functional units using an ε related threshold. Subsequently, a rounding based dynamic programming procedure is performed to handle big functional units and a linear programming based algorithm is performed to handle small functional units, which is followed by the discretization of the continuous linear programming solution. Moreover, through the exploration of the unique nature of our problem, a greedy algorithm is proposed to optimally solve the linear programming formulation in a combinatorial fashion. Further, since patching a salient partitioning solution of big functional units with that of small functional units could lead to a much worse combined solution, a highly efficient enumeration process running in time independent of n is proposed. The experimental results demonstrate that our algorithm runs very fast. It needs only 0.3 second to partition a testcase with 5000 functional units which is more than 10000X faster than the existing algorithm while still reducing the peak power by 7.4%.
ISBN:9781450315739
1450315739
ISSN:1092-3152
DOI:10.1145/2429384.2429537