IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems - Special issue on the 2009 ACM/IEEE international symposium on networks-on-chip
System-level synthesis of memory architecture for stream processing sub-systems of a MPSoC
Proceedings of the 49th Annual Design Automation Conference
Optimizing memory hierarchy allocation with loop transformations for high-level synthesis
Proceedings of the 49th Annual Design Automation Conference
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As technology advances, it becomes feasible to implement a large multiprocessor systems-on-chip (MPSoCs) to satisfy the increased performance demands of embedded applications. The increased complexity of systems leads to an increased power consumption. Reducing the consumption is an important task, considering that the available power may be limited in battery-operated embedded systems. The selection of memory and communication architectures affects the power efficiency of the design. In this paper, we propose a novel approach that enables the energy-aware cosynthesis of both memory and communication architectures for streaming applications. As opposed to earlier techniques, we propose a powerful compile-time analysis of memory access behavior in multiprocessor systems, which adds flexibility in selecting scratch-pad-based memory architectures. We propose and compare three memory/communication synthesis techniques, namely, an optimal mixed integer-linear-programming (ILP)-based cosynthesis technique, a mixed ILP (MILP)-based traditional two-step synthesis approach, where memory and communication synthesis is sequentially performed, and a cosynthesis heuristic that synthesizes energy-efficient hierarchical bus-based communication architectures with guaranteed throughput. Our experimental results on a number of streaming applications show that both the traditional two-step synthesis approach and heuristic result in up to 50% worse power consumption in comparison with our proposed cosynthesis approach. However, on some of the streaming benchmarks, our cosynthesis heuristic approach was able to find optimal or near-optimal results in a much shorter time than the MILP cosynthesis approach.