Design Space Exploration for Architectural Synthesis—A Survey

Shathanaa, R.; Ramasubramanian, N.

doi:10.1007/978-981-10-8636-6_55

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Although the survey provides a comprehensive analysis of HLS software tools (commercial and academic), it does not mention which tools perform multiobjective optimization. e overview presented in [23] mentions strategies to solve the DSE problem by reducing the design space-time. e techniques are compared based on their performance improvement.…”

Section: Related Surveysmentioning

confidence: 99%

Development of Multiobjective High-Level Synthesis for FPGAs

Bulnes

Maldonado

Trujillo

2020

Scientific Programming

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Traditionally, the High-Level Synthesis (HLS) for Field Programmable Gate Array (FPGA) devices is a methodology that transforms a behavioral description, as the timing-independent specification, to an abstraction level that is synthesizable, like the Register Transfer Level. This process can be performed under a framework that is known as Design Space Exploration (DSE), which helps to determine the best design by addressing scheduling, allocation, and binding problems, all three of which are NP-hard problems. In this manner, and due to the increased complexity of modern digital circuit designs and concerns regarding the capacity of the FPGAs, designers are proposing novel HLS techniques capable of performing automatic optimization. HLS has several conflicting metrics or objective functions, such as delay, area, power, wire length, digital noise, reliability, and security. For this reason, it is suitable to apply Multiobjective Optimization Algorithms (MOAs), which can handle the different trade-offs among the objective functions. During the last two decades, several MOAs have been applied to solve this problem. This paper introduces a comprehensive analysis of different MOAs that are suitable to perform HLS for FPGA devices. We highlight significant aspects of MOAs, namely, optimization methods, intermediate structures where the optimizations are performed, HLS techniques that are addressed, and benchmarks and performance assessments employed for experimentation. In addition, we show the analysis of how multiple objectives are optimized currently in the algorithms and which are the objective functions that are optimized. Finally, we provide insights and suggestions to contribute to the solution of major research challenges in this area.

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Section: Related Surveysmentioning

confidence: 99%

Development of Multiobjective High-Level Synthesis for FPGAs

Bulnes

Maldonado

Trujillo

2020

Scientific Programming

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Model-driven design space exploration for multi-robot systems in simulation

et al. 2022

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Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic design space exploration and robustness analysis of multi-robot systems in simulation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission and facilitates the specification of the team’s intelligence. We evaluate ATLAS and demonstrate its effectiveness in three simulated case studies: a healthcare Turtlebot-based mission and two unmanned underwater vehicle missions developed using the Gazebo/ROS and MOOS-IvP robotic platforms, respectively.

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Graph Neural Networks for High-Level Synthesis Design Space Exploration

Ferretti

Cini

Zacharopoulos³

et al. 2022

ACM Trans. Des. Autom. Electron. Syst.

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High-level Synthesis (HLS) Design-Space Exploration (DSE) aims at identifying Pareto optimal synthesis configurations whose exhaustive search is unfeasible due to the design-space dimensionality and the prohibitive computational cost of the synthesis process. Within this framework, we address the design automation problem by proposing graph neural networks that jointly predict acceleration performance and hardware costs of a synthesized behavioral specification given optimization directives. Learned models can be used to rapidly approach the Pareto curve by guiding the DSE, taking into account performance and cost estimates. The proposed method outperforms traditional HLS-driven DSE approaches, by accounting for the arbitrary length of computer programs and the invariant properties of the input. We propose a novel hybrid control and data flow graph representation that enables training the graph neural network on specifications of different hardware accelerators. Our approach achieves prediction accuracy comparable with that of state-of-the-art simulators without having access to analytical models of the HLS compiler. Finally, the learned representation can be exploited for DSE in unexplored configuration spaces by fine-tuning on a small number of samples from the new target domain. The outcome of the empirical evaluation of this transfer learning shows strong results against state-of-the-art baselines in relevant benchmarks.

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Design Space Exploration for Architectural Synthesis—A Survey

Cited by 3 publications

References 19 publications

Development of Multiobjective High-Level Synthesis for FPGAs

Development of Multiobjective High-Level Synthesis for FPGAs

Model-driven design space exploration for multi-robot systems in simulation

Graph Neural Networks for High-Level Synthesis Design Space Exploration

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