Instruction scheduling using MAX-MIN ant system optimization

Proceedings of the 43rd Annual Conference on Design Automation - DAC '06

DeRenzi

et al. 2006

Self Cite

Design space exploration during high level synthesis is often conducted through ad-hoc probing of the solution space using some scheduling algorithm. This is not only time consuming but also very dependent on designer's experience. We propose a novel design exploration method that exploits the duality between the time and resource constrained scheduling problems. Our exploration automatically constructs a high quality time/area tradeoff curve in a fast, effective manner. It uses the MAX-MIN ant colony optimization to solve both the time and resource constrained scheduling problems. We switch between the time and resource constrained algorithms to quickly traverse the design space. Compared to using force directed scheduling exhaustively at every time step, our algorithm provides a significant solution quality savings (average 17.3% reduction of resource counts) with similar run time on a comprehensive benchmark suite constructed with classic and reallife samples. Our algorithms scale well over different applications and problem sizes.

“…• It generates better quality scheduling results that are close to the optimal with good stability for both the TCS and RCS problems [13].…”

Section: Ant Colony Optimizationsmentioning

confidence: 90%

Section: Ant Colony Optimizationsmentioning

confidence: 99%

See 1 more Smart Citation

Design space exploration using time and resource duality with the ant colony optimization

Proceedings of the 43rd Annual Conference on Design Automation - DAC '06

DeRenzi

et al. 2006

Self Cite

“…In order to select suitable TCS and RCS algorithms, we studied different scheduling approaches for the two problems, including the popularly used forcedirected scheduling (FDS) for the TCS problem [Paulin and Knight 1987], various list scheduling heuristics, and the recently proposed ant colony optimization (ACO)-based instruction scheduling algorithms [Wang et al 2005;Wang et al to appear]. We found that ACO-based scheduling algorithms offer the following major benefits over FDS, several variants of list scheduling, and simulated annealing [Wang et al to appear]: (1) ACO-based scheduling algorithms generate better-quality results that are close to optimal, with good stability for both the TCS and RCS problems; and (2) ACO-based methods provide reasonable runtime; (3) as a population-based method, the ACO-based TCS approach naturally provides multiple alternative solutions.…”

Section: Ant Colony Optimizations For Time-and Resource-constrained Smentioning

confidence: 99%

“…Unfortunately, in the interests of space, we can only give a general introduction on the ACO formulation for the TCS problem. For a complete treatment of the algorithms, including detailed discussion on their implementation, applicability, complexity, extensibility, parameter selection, and performance, please refer to our previous publications [Wang et al 2005;Wang et al to appear].…”

Section: Time-and Resource-constrained Scheduling Formulationmentioning

confidence: 99%

Exploring time/resource trade-offs by solving dual scheduling problems with the ant colony optimization

ACM Trans. Des. Autom. Electron. Syst.

DeRenzi

et al. 2007

Self Cite

Design space exploration during high-level synthesis is often conducted through ad hoc probing of the solution space using some scheduling algorithm. This is not only time consuming but also very dependent on designer's experience. We propose a novel design exploration method that exploits the duality of time-and resource-constrained scheduling problems. Our exploration automatically constructs a time/area tradeoff curve in a fast, effective manner. It is a general approach and can be combined with any high-quality scheduling algorithm. In our work, we use the max-min ant colony optimization technique to solve both time-and resource-constrained scheduling problems. Our algorithm provides significant solution-quality savings (average 17.3% reduction of resource counts) with similar runtime compared to using force-directed scheduling exhaustively at every time step. It also scales well across a comprehensive benchmark suite constructed with classic and real-life samples.

Operation Scheduling: Algorithms and Applications

Kastner

High-Level Synthesis

Self Cite

Operation scheduling (OS) is an important task in the high-level synthesis process. An inappropriate scheduling of the operations can fail to exploit the full potential of the system. In this chapter, we try to give a comprehensive coverage on the heuristic algorithms currently available for solving both timing and resource constrained scheduling problems. Besides providing a broad survey on this topic, we focus on some of the most popularly used algorithms, such as List Scheduling, Force-Directed Scheduling and Simulated Annealing, as well as the newly introduced approach based on the Ant Colony Optimization meta-heuristics. We discuss in details on their applicability and performance by comparing them on solution quality, performance stability, scalability, extensibility, and computation cost. Moreover, as an application of operation scheduling, we introduce a novel uniformed design space exploration method that exploits the duality of the time and resource constrained scheduling problems, which automatically constructs a high quality time/area tradeoff curve in a fast, effective manner.Keywords: Design space exploration, Ant colony optimization, Instruction scheduling, MAX-MIN ant system IntroductionAs fabrication technology advances and transistors become more plentiful, modern computing systems can achieve better system performance by increasing the amount of computation units. It is estimated that we will be able to integrate more than a half billion transistors on a 468 mm 2 chip by the year of 2009 [38]. This yields tremendous potential for future computing systems, however, it imposes big challenges on how to effectively use and design such complicated systems.As computing systems become more complex, so do the applications that can run on them. Designers will increasingly rely on automated design tools in order P. Coussy and A. Morawiec (eds.) High-Level Synthesis.