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Automatic Specification Granularity Tuning for Design Space Exploration

Algorithm Design Environments (ADE), such as Simulink, have been shown to be efficient for development, analysis, and evaluation of algorithms. Recent tools propose to facilitate algorithm/architecture co-design by bridging the gap from ADE to System-Level Design Environments (SLDE) through automatic synthesis from algorithm models to SLDL specifications. With the wide range of block characteristic (from simple logic functions to complex kernels) in the algorithm model, however, it is challenging to select a suitable compositional granularity for SLD Language (SLDL) blocks in the synthesized specification. A high volume of SLDL blocks of little computation will increase the number of mapping possibilities, whereas large blocks with heavy computation on the other hand allow inter-block fusion reducing the computational demands in the overall specification yet sacrificing the mapping flexibility.

In this paper, we introduce an automatic specification granularity tuning mechanism to determine the granularity in the synthesized specification model hierarchy guided by the computational demands of algorithm blocks. Our granularity selection significantly simplifies the early design space exploration as only a meaningful block decomposition is exposed in the synthesized specification. It leads to an overall system with less computational demands by leveraging the block fusion capabilities in the ADE. At the same time our granularity decision ensures that sufficient flexibility remains in the system for exploring heterogeneous mapping of the algorithm. Our results on real world examples show that specification models can be synthesized with 80% efficiency through block fusion with 70-90% fewer but coarser grained blocks.

Appeared in:
Proceedings of the ACM/IEEE Conference on Design, Automation & Test in Europe (DATE)
Presentation Place:
Dresden, Germany
Related Research:  Algo2Spec: Integrating Algorithm Development and System-level Design

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