Scalable Embedded Systems: Towards the Convergence of High-Performance and Embedded Computing

Embedded System toolchains are highly customized for a specific System-on-Chip (SoC). When the application needs more performance, the designer is typically forced to adopt a new SoC and possibly another toolchain. The rationale for not scaling performance by using, e.g., two SoCs, is that maintining most of the operations on-chip may allow for higher energy efficiency. We are exploring the feasibility and trade-offs of designing and manufacturing a new Single Board Computer (SBC) that could serve flexibly for a number of current and future applications, by allowing scalability through clusters of SBCs while keeping the same programming model for the SBC. This board is based on FPGAs and embedded processors, and its key points are: i) a fast custom interconnect for board-to-board communication and ii) an easily programmable environment which would allow both the off-loading of code into accelerators (either soft-IP blocks or hard-IP blocks) and, at the same time, the distribution of computation across boards. A key challenge to successfully deploying this paradigm is to properly distribute the threads across several boards without the explicit intervention of the programmer. In this paper we describe how to dynamically and efficiently distribute the computational threads in symbiosis with an appropriate memory model to allow the system scalability, so that we can double the performance by simply connecting two boards without i) changing the basic hardware components (e.g., to a different System-On-Chip) and ii) changing the programming model to follow the vendor specific toolchain. Our approach is to reduce data movement across boards. Our initial experiments have confirmed the feasibility of our approach