Templatized Fused Vector Floating-Point Dot Product for High-Level Synthesis

Abstract

Machine-learning accelerators rely on floating-point matrix and vector multiplication kernels. To reduce their cost, customized many-term fused architectures are preferred, which improve the latency, power, and area of the designs. In this work, we design a parameterized fused many-term floating-point dot product architecture that is ready for high-level synthesis. In this way, we can exploit the efficiency offered by a well-structured fused dot-product architecture and the freedom offered by high-level synthesis in tuning the design’s pipeline to the selected floating-point format and architectural constraints. When compared with optimized dot-product units implemented directly in RTL, the proposed design offers lower-latency implementations under the same clock frequency with marginal area savings. This result holds for a variety of floating-point formats, including standard and reduced-precision representations.

A High-Level Synthesis Library for Synthesizing Efficient and Functional-Safe CNN Dataflow Accelerators

“…The floating-point operators integrated into the library are based on the FastFloat4HLS [37] open-source HLS library for FP arithmetic that support custom datatypes as well as dotproduct fused operations that -to the best of our knowledgehave not yet been integrated into any other open source HLS CNN model. The HLS implementation for fused dot-product operations that has been presented in [38] and is integrated in the FastFloat4HLS library, allows the design of any term fused dot-product units. This, combined with the support for arbitrary datatypes, allows the implementation of custom optimized operators depending on the characteristics of the CNN layer.…”