Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Artificial Neural Networks: Theory and Applications
Artificial Neural Networks: Theory and Applications
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
Exploring "temperature-aware" design in low-power MPSoCs
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Journal of VLSI Signal Processing Systems
Temperature aware task scheduling in MPSoCs
Proceedings of the conference on Design, automation and test in Europe
Temperature control of high-performance multi-core platforms using convex optimization
Proceedings of the conference on Design, automation and test in Europe
Temperature-Aware Distributed Run-Time Optimization on MP-SoC Using Game Theory
ISVLSI '08 Proceedings of the 2008 IEEE Computer Society Annual Symposium on VLSI
Proceedings of the 46th Annual Design Automation Conference
Utilizing predictors for efficient thermal management in multiprocessor SoCs
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 20th symposium on Great lakes symposium on VLSI
Hotspot: acompact thermal modeling methodology for early-stage VLSI design
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
The weighted average constraint
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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The benefits of combinatorial optimization techniques for the solution of real-world industrial problems are an acknowledged evidence; yet, the application of those approaches to many practical domains still encounters active resistance by practitioners, in large part due to the difficulty to come up with accurate declarative representations. We propose a simple and effective technique to bring hard-to-describe systems within the reach of Constraint Optimization methods; the goal is achieved by embedding into a combinatorial model a softcomputing paradigm, namely Neural Networks, properly trained before their insertion. The approach is flexible and easy to implement on top of available Constraint Solvers. To provide evidence for the viability of the proposed method, we tackle a thermal aware task allocation problem for a multi-core computing platform.