A dataflow architecture with distributed control for DNN acceleration

The increasing demand for edge computing requires effective Deep Neural Network (DNN) accelerators that are suitable for resource-limited environments. This paper presents a new method that uses distributed control methodology for DNN acceleration on edge devices. Our architecture offers significant improvements over a similar architecture without such feature, including a remarkable reduction in memory requirements by up to 7×, along with notable speedups of up to 7.42× in DNN processing. Additionally, our design reaches energy efficiency of a maximum of 4300 MOPS/W, demonstrating its potential to address resource constraints while improving DNN performance on edge platforms.