19015

FBLAS: Streaming Linear Algebra on FPGA

Tiziano De Matteis, Johannes de Fine Licht, Torsten Hoefler
Department of Computer Science, ETH Zurich, Switzerland
arXiv:1907.07929 [cs.DC], (19 Jul 2019)

@misc{matteis2019fblas,

   title={FBLAS: Streaming Linear Algebra on FPGA},

   author={Tiziano De Matteis and Johannes de Fine Licht and Torsten Hoefler},

   year={2019},

   eprint={1907.07929},

   archivePrefix={arXiv},

   primaryClass={cs.DC}

}

Energy efficiency is one of the primary concerns when designing large scale computing systems. This makes reconfigurable hardware an attractive alternative to load-store architectures, as it allows eliminating expensive control and data movement overheads in computations. In practice, these devices are often not considered in the high-performance computing community, due to the steep learning curve and low productivity of hardware design, and the lack of available library support for fundamental operations. With the introduction of high-level synthesis (HLS) tools, programming hardware has become more accessible, but optimizing for these architectures requires factoring in new transformations and trade-offs between hardware resources and computational performance. We present FBLAS, an open source implementation of BLAS for FPGAs. FBLAS is implemented with HLS, enabling reusability, maintainability, and portability across FPGAs, and easy integration with existing software and hardware codes. By using the work-depth model, we capture the space/time trade-off of designing linear algebra circuits, allowing modules to be optimized within performance or resource constraints. Module interfaces are designed to natively support streaming communication across on-chip connections, allowing them to be composed to reduce off-chip communication. With the methodologies used to design FBLAS, we hope to set a precedent for FPGA library design, and contribute to the toolbox of customizable hardware components that is necessary for HPC codes to start productively targeting reconfigurable platforms.
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