Broadening the exploration of the accelerator design space in embedded scalable platforms

Abstract

Accelerators are specialized hardware designs that generally guarantee two to three orders of magnitude higher energy efficiency than general-purpose processor cores for their target computational kernels. To cope with the complexity of integrating many accelerators into heterogeneous systems, we have proposed Embedded Scalable Platforms (ESP) that combines a flexible architecture with a companion systemlevel design (SLD) methodology. In ESP, we leverage high-level synthesis (HLS) to expedite the design of accelerators, improve the process of design-space exploration (DSE), and promote the reuse of accelerators across different target systems-on-chip (SoCs). HLS tools offer a powerful set of parameters, known as knobs, to optimize the architecture of an accelerator and evaluate different trade-offs in terms of performance and costs. However, exploring a large region of the design space and identifying a rich set of Pareto-optimal implementations are still complex tasks. The standard knobs, in fact, operate only on loops and functions present in the high-level specifications, but they cannot work on other key aspects of SLD such as I/O bandwidth, on-chip memory organization, and trade-offs between the size of the local memory and the granularity at which data is transferred and processed by the accelerators. To address these limitations, we augmented the set of HLS knobs for ESP with three additional knobs, named eXtended Knobs (XKnobs). We used the XKnobs for exploring two selected kernels of the wide-area motion imagery (WAMI) application. Experimental results show that the DSE is broadened by up to 8.5× for the performance figure (latency) and 3.5× for the implementation costs (area) compared to use only the standard knobs.