Simultaneous peak and average power minimization during datapath scheduling for DSP processors
Proceedings of the 13th ACM Great Lakes symposium on VLSI
A Framework for Energy and Transient Power Reduction during Behavioral Synthesis
VLSID '03 Proceedings of the 16th International Conference on VLSI Design
An Evolutionary Design Algorithm for Ring-based SDH optical core networks
BT Technology Journal
ILP models for simultaneous energy and transient power minimization during behavioral synthesis
ACM Transactions on Design Automation of Electronic Systems (TODAES)
EUC'07 Proceedings of the 2007 conference on Emerging direction in embedded and ubiquitous computing
Minimizing leakage power in aging-bounded high-level synthesis with design time multi-Vth assignment
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Parametric yield driven resource binding in behavioral synthesis with multi-Vth/Vdd library
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Journal of Electrical and Computer Engineering - Special issue on ESL Design Methodology
A clock control strategy for peak power and RMS current reduction using path clustering
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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The work presented in this paper focuses on behavioral level power optimization. Specifically, we address the problem of scheduling a data-flow graph under latency constraints. We have developed an integer linear program (ILP) model and a modified force-directed scheduling (MFDS) that minimize the peak power while satisfying timing constraints. Our integer linear programming method extends the traditional ILP approach that minimizes resources to include peak power consideration while adding extensions for multi-cycle and pipelined arithmetic components. In our benchmark results, the peak power is reduced after scheduling based on ILP method and MFDS algorithm and is reduced significantly after scheduling and pipelining are both applied. The results obtained by the heuristic-based algorithms (MFDS) match very well with those obtained by the integer linear programming (ILP) methods. While the results obtained by heuristic-based algorithm are approximate, the results obtained by the integer linear programming methods are optimal. However, the heuristic-based algorithm is faster than the integer linear programming methods.