A System-Level Performance Analysis of On-Device Learning on an Ultra-Low-Power Edge System

Training Deep Neural Networks (DNNs) on ultra-low-power processing systems is often hindered by the memory-intensive nature of backpropagation. This paper examines the trade-offs between memory and computational costs when mapping DNN training onto an ultra-low-power RISC-V-based System-on-Chip (SoC) with an 8-core accelerator, 1.5 MB on-chip memory, and a 32 MB off-chip memory. To this end, we develop a framework that automates the deployment of complete training graphs across heterogeneous SoCs. The generated code interleaves memory transfer operations with layer-wise processing functions, which are executed either on the host CPU or, for convolutional layers, on the multi-core accelerator. Overall, our work is the first to provide a full system-level analysis of end-To-end DNN training on OS-less, memory-constrained (32 MB of off-chip memory) edge systems, and we show that computation is the primary bottleneck by accounting for up to 91% of the total energy consumption.