Convex separable optimization is not much harder than linear optimization
Journal of the ACM (JACM)
LEneS: task scheduling for low-energy systems using variable supply voltage processors
Proceedings of the 2001 Asia and South Pacific Design Automation Conference
Task scheduling and voltage selection for energy minimization
Proceedings of the 39th annual Design Automation Conference
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
IEEE Computational Science & Engineering
Design of an Optimal Loosely Coupled Heterogeneous Multiprocessor System
EDTC '96 Proceedings of the 1996 European conference on Design and Test
Visual assessment of a real-time system design: a case study on a CNC controller
RTSS '96 Proceedings of the 17th IEEE Real-Time Systems Symposium
IEEE Transactions on Parallel and Distributed Systems
An efficient voltage scaling algorithm for complex SoCs with few number of voltage modes
Proceedings of the 2004 international symposium on Low power electronics and design
Proceedings of the conference on Design, automation and test in Europe
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Power-Aware Real-Time Scheduling upon Dual CPU Type Multiprocessor Platforms
OPODIS '08 Proceedings of the 12th International Conference on Principles of Distributed Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 21st edition of the great lakes symposium on Great lakes symposium on VLSI
Journal of Systems Architecture: the EUROMICRO Journal
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In this paper, we tackle the problem of minimizing the energy consumption of multiple-processors-core system using multiple variable supply voltages. Given a set of tasks and their dependency, and a set of target processors, we propose an optimal formulation of carrying out task allocation on processors, task scheduling and voltage assignment for each task concurrently using mixed integer nonlinear programming (MINLP). An iterative divide-and conquer approach is introduced to reduce the run time for large set of tasks. Experimental results show that by using the proposed approach, significant energy reductions are achieved over the existing methods.