Multi-objective co-exploration of source code transformations and design space architectures for low-power embedded systems

Agosta, Giovanni; Palermo, Gianluca; Silvano, Cristina

doi:10.1145/967900.968080

Cited by 13 publications

(10 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples include design explorations with multiple objectives used in transforming a system implementation to an optimal one [1], ForSyDe methodology to transform abstract system design to an implementation through refinement of communication and clock domain [12], and the methods to transform analysis models to object-oriented models in [3,8]. Although these transformations support instance-based transformations and integration of analysis, eliminating their dedications to particular models and hard-coded algorithms is essential for them to be applicable to the software development for derivative vehicle systems, which must deal with multiple aspects and stages.…”

Section: Related Workmentioning

confidence: 99%

Model Transformation for High-Integrity Software Development in Derivative Vehicle Control System Design

Feinstein

Nair

Thornton

2007

10th IEEE High Assurance Systems Engineering Symposium (HASE'07)

View full text Add to dashboard Cite

Derivative design benefits vehicle system development as a vehicle product typically has a large number of variations.To ensure high-integrity control of a vehicle system, the software for control derivatives must be developed consistently and trustfully across the product variations. Model transformation is a promising technology to support such software development. In this paper, we present our study of using a research model transformation tool GReAT in the software development of an enhanced cruise control system. Our results showed that current model transformation techniques partially meet the model transformation needs of high-integrity, derivative control software development. Further research is required to provide capability such as analysis integration and instance-directed rule executions.

show abstract

Section: Related Workmentioning

confidence: 99%

Model Transformation for High-Integrity Software Development in Derivative Vehicle Control System Design

Feinstein

Nair

Thornton

2007

10th IEEE High Assurance Systems Engineering Symposium (HASE'07)

View full text Add to dashboard Cite

show abstract

“…Starting from our preliminary work in [4], the compiler/architecture design spaces are explored concurrently and energy/delay trade-offs have been analyzed. The main contributions of our work can be summarized as:…”

Section: Motivations Of Our Workmentioning

confidence: 99%

“…The design space co-exploration framework described in this section has been previously proposed in [4]. The structure of the framework, shown in Fig.…”

Section: Design Space Co-exploration Frameworkmentioning

confidence: 99%

“…Only recently, some approaches [1][2][3][4] have analyzed the co-exploration of the compilation space and the architectural design space from the energy/delay combined perspective. Although the works in [1,2] investigate the influence of compiler optimization on the architectural exploration, the source code is first optimized independent of the target architecture, then the architecture is explored.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, our framework has been proposed in [4] to jointly explore the set of all HW/SW configurations of the SOC platform defined in the combined System-Level Design Space: S SD = S T × S A , where S T is the space of program transformations and S A is the space of architectural parameters. From the hardware side, we explore the architecture design space (S A ) for parameterized microprocessor-based systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient architecture/compiler co-exploration using analytical models

Silvano

Agosta

Palermo

2007

Des Autom Embed Syst

Self Cite

View full text Add to dashboard Cite

The hardware/software co-exploration is a critical phase for a broad range of embedded platforms based on the System-On-Chip approach. Traditionally, the compilation and the architectural design sub-spaces have been explored independently. Only recently, some approaches have analyzed the problem of the concurrent exploration of the compilation/architecture sub-spaces. This paper proposes a framework to support the co-exploration phase of the design space composed of architectural parameters and source program transformations. The objective space is multi-dimensional, including conflicting objectives such as energy and delay. In the proposed framework, heuristic co-exploration techniques based on Pareto Simulated Annealing (PSA) have been used to efficiently explore the architecture/compiler co-design space. A first result of this paper consists of showing how the architecture/compiler co-exploration can be more effective than a traditional two-phase exploration. Since the co-exploration space is quite large, to speed up the co-exploration phase by several orders of magnitude over simulation-based approaches, a methodology based on analytical models has been introduced in the co-exploration framework. The goal of analytical models is to quickly evaluate energy/delay metrics at the systems level, while maintaining accuracy with respect to simulation-based co-exploration. The proposed co-exploration framework has been applied to a parameterized SoC superscalar architecture during the execution of a selected set of multimedia applications.

show abstract

Finding near‐perfect parameters for hardware and code optimizations with automatic multi‐objective design space explorations

Jahr

Calborean

Vintan

et al. 2012

Concurrency and Computation

View full text Add to dashboard Cite

SummaryIn the design process of computer systems or processor architectures, typically many different parameters are exposed to configure, tune, and optimize every component of a system. For evaluations and before production, it is desirable to know the best setting for all parameters. Processing speed is no longer the only objective that needs to be optimized; power consumption, area, and so on have become very important. Thus, the best configurations have to be found in respect to multiple objectives. In this article, we use a multi‐objective design space exploration tool called Framework for Automatic Design Space Exploration (FADSE) to automatically find near‐optimal configurations in the vast design space of a processor architecture together with a tool for code optimizations and hence evaluate both automatically. As example, we use the Grid ALU Processor (GAP) and its postlink optimizer called GAPtimize, which can apply feedback‐directed and platform‐specific code optimizations. Our results show that FADSE is able to cope with both design spaces. Less than 25% of the maximal reasonable hardware effort for the scalable elements of the GAP is enough to achieve the processor's performance maximum. With a performance reduction tolerance of 10%, the necessary hardware complexity can be further reduced by about two‐thirds. The found high‐quality configurations are analyzed, exhibiting strong relationships between the parameters of the GAP, the distribution of complexity, and the total performance. These performance numbers can be improved by applying code optimizations concurrently to optimizing the hardware parameters. FADSE can find near‐optimal configurations by effectively combining and selecting parameters for hardware and code optimizations in a short time. The maximum observed speedup is 15%. With the use of code optimizations, the maximum possible reduction of the hardware resources, while sustaining the same performance level, is 50%.Copyright © 2012 John Wiley & Sons, Ltd.

show abstract

Multi-objective co-exploration of source code transformations and design space architectures for low-power embedded systems

Cited by 13 publications

References 25 publications

Model Transformation for High-Integrity Software Development in Derivative Vehicle Control System Design

Model Transformation for High-Integrity Software Development in Derivative Vehicle Control System Design

Efficient architecture/compiler co-exploration using analytical models

Finding near‐perfect parameters for hardware and code optimizations with automatic multi‐objective design space explorations

Contact Info

Product

Resources

About