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Reconfigurable systems can offer the high spatial parallelism and fine-grained, bit-level resource control traditionally associated with hardware implementations, along with the flexibility and adaptability characteristic of software. While reconfigurable systems create new opportunities for engineering and delivering high-performance programmable systems, the traditional approaches to programming and managing computations used for hardware systems (e.g., Verilog, VHDL) and software systems (e.g., C, Fortran, Java) are inappropriate and inadequate for exploiting reconfigurable platforms. To address this need, we develop a stream-oriented compute model, system architecture, and execution patterns which can capture and exploit the parallelism of spatial computations while simultaneously abstracting software applications from hardware details (e.g., timing, device capacity, and microarchitectural implementation details) and consequently allowing applications to scale to exploit newer, larger, and faster hardware platforms. Further, we describe hardware and software techniques that make this late-bound platform mapping viable and efficient.

No abstract

The SCORE compute model uses fixed-size, virtual compute and memory pages connected by stream links to capture the definition of a computation abstracted from the detailed size of the physical hardware.When the number of physical compute pages is smaller than the number of virtual compute pages in the abstract computation graph, the design is time-multiplexed onto the available physical hardware. A key component of this strategy is an automatic scheduler that selects the temporal sequencing of virtual resources onto the physical device. We describe a quasistatic scheduling strategy that retains the full semantic power of the dynamic SCORE flow graph while taking advantage of static scheduling techniques at program load time to hoist most of the computational work out of the inner scheduling loops. This strategy reduces online scheduling work per reconfiguration epoch by an order of magnitude. In addition, a more global perspective available from offline-scheduling improves schedule quality, resulting in a net reduction of total execution time by 46-81%.