Force-directed scheduling in automatic data path synthesis
DAC '87 Proceedings of the 24th ACM/IEEE Design Automation Conference
On the Complexity of Scheduling Problems for Parallel/Pipelined Machines
IEEE Transactions on Computers
Generating Linear Extensions Fast
SIAM Journal on Computing
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Future Generation Computer Systems
A comparison of list schedules for parallel processing systems
Communications of the ACM
A fast approach to computing exact solutions to the resource-constrained scheduling problem
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Synthesis and Optimization of Digital Circuits
Synthesis and Optimization of Digital Circuits
Graph-partitioning based instruction scheduling for clustered processors
Proceedings of the 34th annual ACM/IEEE international symposium on Microarchitecture
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
A super-scheduler for embedded reconfigurable systems
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Multi-heuristic list scheduling genetic algorithm for task scheduling
Proceedings of the 2003 ACM symposium on Applied computing
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design space exploration using time and resource duality with the ant colony optimization
Proceedings of the 43rd annual Design Automation Conference
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Mobility prediction based on an ant system
Computer Communications
Overview of metaheuristics methods in compilation
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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Instruction scheduling is a fundamental step for mapping an application to a computational device. It takes a behavioral application specification and produces a schedule for the instructions onto a collection of processing units. The objective is to minimize the completion time of the given application while effectively utilizing the computational resources. The instruction scheduling problem is NP-hard, thus effective heuristic methods are necessary to provide a qualitative scheduling solution. In this paper, we present a novel instruction scheduling algorithm using MAX-MIN Ant System Optimization approach. The algorithm utilizes a unique hybrid approach by combining the ant system meta-heuristic with list scheduling, where the local and global heuristics are dynamically adjusted to the input application in an iterative manner. Compared with force-directed scheduling and a number of different list scheduling heuristics, our algorithm generates better results over all the tested benchmarks with better stability. Furthermore, by solving the test samples optimally using ILP formulation, we show that our algorithm consistently achieves a near optimal solution.