Meta-model Assisted Optimization for Design Space Exploration of Multi-Processor Systems-on-Chip

Mariani, Giovanni; Palermo, Gianluca; Silvano, Cristina; Zaccaria, Vittorio

doi:10.1109/dsd.2009.154

Cited by 7 publications

(3 citation statements)

References 18 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mariani et al [17] use neural networks to accelerate the DSE process. The authors predict through neural networks if an individual is worth simulating or not, but still the knowledge of an architect is not used.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective optimisations for a superscalar architecture with selective value prediction

Gellért¹,

Calborean²,

Vintan³

et al. 2012

IET Comput. Digit. Tech.

View full text Add to dashboard Cite

This work extends an earlier manual design space exploration (DSE) of the authors' developed selective load value prediction-based superscalar architecture to the L2 unified cache. After that the authors perform an automatic DSE using a special developed software tool by varying several architectural parameters. The goal is to find optimal configurations in terms of cycles per instruction and energy consumption. By varying 19 architectural parameters, as the authors proposed, the design space is over 2.5 millions of billions configurations which obviously means that only a heuristic search can be considered. Therefore the authors propose different methods of automatic DSE based on their developed framework for automatic design space exploration which allow them to evaluate only 2500 configurations of the above mentioned huge design space! The experimental results show that their automatic DSE provides significantly better configurations than the previous manual DSE approach, considering the proposed multi-objective approach.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-objective optimisations for a superscalar architecture with selective value prediction

Gellért¹,

Calborean²,

Vintan³

et al. 2012

IET Comput. Digit. Tech.

View full text Add to dashboard Cite

show abstract

“…Based on NSGA-II, Mariani et al [16,18] use an Artificial Neural Network (ANN) to predict the quality of individuals, which is then used to decide if they should be simulated or not. ANNs are also integrated into a multi-level model [17].…”

Section: Related Workmentioning

confidence: 99%

Boosting Design Space Explorations with Existing or Automatically Learned Knowledge

Jahr

Calborean

Vintan

et al. 2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. During development, processor architectures can be tuned and configured by many different parameters. For benchmarking, automatic design space explorations (DSEs) with heuristic algorithms are a helpful approach to find the best settings for these parameters according to multiple objectives, e.g. performance, energy consumption, or real-time constraints. But if the setup is slightly changed and a new DSE has to be performed, it will start from scratch, resulting in very long evaluation times.To reduce the evaluation times we extend the NSGA-II algorithm in this article, such that automatic DSEs can be supported with a set of transformation rules defined in a highly readable format, the fuzzy control language (FCL). Rules can be specified by an engineer, thereby representing existing knowledge. Beyond this, a decision tree classifying high-quality configurations can be constructed automatically and translated into transformation rules. These can also be seen as very valuable result of a DSE because they allow drawing conclusions on the influence of parameters and describe regions of the design space with high density of good configurations.Our evaluations show that automatically generated decision trees can classify near optimal configurations for the hardware parameters of the Grid ALU Processor (GAP) and M-Sim 2. Further evaluations show that automatically constructed transformation rules can reduce the number of evaluations required to reach the same quality of results as without rules by 43%, leading to a significant saving of time of about 25%. In the demonstrated example using rules also leads to better results.

show abstract

“…For example, Gramacy et al use them to optimize the design of the NASA re-entry vehicles [10], while Mariani et al use them in the design of multi-processor systems-on-chip [20].…”

Section: Npmentioning

confidence: 99%

Testing elastic systems with surrogate models

Gambi

Hummer

Dustdar

2013

2013 1st International Workshop on Combining Modelling and Search-Based Software Engineering (CMSBSE)

View full text Add to dashboard Cite

Abstract-We combine search-based test case generation and surrogate models for black-box system testing of elastic systems. We aim to efficiently generate tests that expose functional errors and performance problems related to system elasticity.Elastic systems dynamically change their resources allocation to provide consistent quality of service in face of workload fluctuations. However, their ability to adapt could be a double edged sword if not properly designed: They may fail to acquire the right amount of resources or even fail to release them. Blackbox system testing may expose such problems by stimulating system elasticity with suitable sequences of interactions. However, finding such sequences is far from trivial because the number of possible combinations of requests over time is unbounded.In this paper, we analyze the problem of generating test cases for elastic systems, we cast it as a search-based optimization combined with surrogate models, and present the conceptual framework that supports its execution.

show abstract

Meta-model Assisted Optimization for Design Space Exploration of Multi-Processor Systems-on-Chip

Cited by 7 publications

References 18 publications

Multi-objective optimisations for a superscalar architecture with selective value prediction

Multi-objective optimisations for a superscalar architecture with selective value prediction

Boosting Design Space Explorations with Existing or Automatically Learned Knowledge

Testing elastic systems with surrogate models

Contact Info

Product

Resources

About