As the System-on-Chip (SoC) complexity increases, hardware/software co-design plays an important role to improve design productivity, reduce time to market, and optimize the overall results. Consequently, there is a high interest in providing rapid system validation in such a paradigm to achieve the aforementioned objectives. There exist in previous works prototyping techniques related to the development phase. FPGA-based prototyping has the benefits of enabling HW/SW integration and system validation after the Register Transfer Level (RTL) implementation is available while virtual platforms provide capabilities to accelerate software development with higher level functional models, e.g. Transaction Level Modeling (TLM). In this paper, we propose a hybrid prototyping methodology which takes advantage of virtual and FPGA-based prototyping in a single framework. We aim to provide a rapid and flexible system validation solution for HW/SW co-design at various stages of development based on the availability of TLM and RTL implementations. The proposed methodology allows online and offline performance analysis and debugging for early feedback in HW/SW architecture exploration. This was evaluated in the experiments with a neural network processor as a case study.
The current trend in embedded computing consists in increasing the number of processing resources on a chip. Following this paradigm, cluster-based many-core accelerators with a shared hierarchical memory have emerged. Handling synchronizations on these architectures is critical since parallel implementations speed-ups of embedded applications strongly depend on the ability to exploit the largest possible number of cores while limiting task management overhead. This article presents the combination of a low-overhead complete runtime software and a flexible hardware accelerator for synchronizations called HARS (Hardware-Assisted Runtime Software). Experiments on a multicore test chip showed that the hardware accelerator for synchronizations has less than 1% area overhead compared to a cluster of the chip while reducing synchronization latencies (up to 2.8 times compared to a
test-and-set
implementation) and contentions. The runtime software part offers basic features like memory management but also optimized execution engines to allow the easy and efficient extraction of the parallelism in applications with multiple programming models. By using the hardware acceleration as well as a very low overhead task scheduling software technique, we show that HARS outperforms an optimized state-of-the-art task scheduler by 13% for the execution of a parallel application.
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