Taxonomy and Benchmarking of Precision-Scalable MAC Arrays Under Enhanced DNN Dataflow Representation

Understand

Reduced-precision and variable-precision multiply-accumulate (MAC) operations provide opportunities to significantly improve energy efficiency and throughput of DNN accelerators with no/limited algorithmic performance loss, paving a way towards deploying AI applications on resource-constraint edge devices.

• Accordingly, various precision-scalable MAC array (PSMA) architectures were proposed recently.
• However, it is difficult to make a fair comparison between those alternatives, as each proposed PSMA is demonstrated in different systems and technologies.
• This work aims to provide a clear view of the design space of PSMA and offer insights for selecting the optimal architectures based on designers' needs.