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Separation Logic-Assisted Code Transformations for Efficient High-Level Synthesis
<span title="">2014</span>
<i title="IEEE">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hss6rlelz5by5nc6dnosoj2luu" style="color: black;">2014 IEEE 22nd Annual International Symposium on Field-Programmable Custom Computing Machines</a>
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The capabilities of modern FPGAs permit the mapping of increasingly complex applications into reconfigurable hardware. High-level synthesis (HLS) promises a significant shortening of the FPGA design cycle by raising the abstraction level of the design entry to high-level languages such as C/C++. Applications using dynamic, pointer-based data structures and dynamic memory allocation, however, remain difficult to implement well, yet such constructs are widely used in software. Automated
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... ons that aim to leverage the increased memory bandwidth of FPGAs by distributing the application data over separate banks of on-chip memory are often ineffective in the presence of dynamic data structures, due to the lack of an automated analysis of pointer-based memory accesses. In this work, we take a step towards closing this gap. We present a static analysis for pointer-manipulating programs which automatically splits heap-allocated data structures into disjoint, independent regions. The analysis leverages recent advances in separation logic, a theoretical framework for reasoning about heap-allocated data which has been successfully applied in recent software verification tools. Our algorithm focuses on dynamic data structures accessed in loops and is accompanied by automated source-to-source transformations which enable automatic loop parallelization and memory partitioning by off-the-shelf HLS tools. We demonstrate the successful loop parallelization and memory partitioning by our tool flow using three real-life applications which build, traverse, update and dispose dynamically allocated data structures. Our case studies, comparing the automatically parallelized to the non-parallelized HLS implementations, show an average latency reduction by a factor of 2.5 across our benchmarks.
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