CAMEL: Co-Designing AI Models and Embedded DRAMs for Efficient On-Device Learning

The emergence of the Internet of Things (IoT) has resulted in a remarkable amount of data generated on edge devices, which are often processed using AI algorithms. On-device learning enables edge platforms to continually adapt the AI models to user personal data and further allows for a better service quality. However, AI training on resource-limited devices is extremely difficult because of the intensive computing workload and the significant amount of on-chip memory consumption exacted by deep neural networks (DNNs). To mitigate this, we propose to use embedded dynamic random-access memory (eDRAM) as the main storage medium of training data. Compared with static random-access memory (SRAM), eDRAM introduces more than $2\times$ improvement on storage density, enabling reduced off-chip memory traffic. However, to keep the stored data intact, eDRAM is required to perform the power-hungry data refresh operations. eDRAM refresh can be eliminated if the data is stored for a period of time that is shorter than the eDRAM retention time. To achieve this, we design a novel reversible DNN architecture that enables a significantly reduced data lifetime during the training process and removes the need for eDRAM refresh. We further design an efficient on-device training engine, termed~\textit{CAMEL}, that uses eDRAM as the main on-chip memory. CAMEL enables the intermediate results during training to fit fully in on-chip eDRAM arrays and completely eliminates the off-chip DRAM traffic during the training process. We evaluate our CAMEL system on multiple DNNs with different datasets, demonstrating a more than $3\times$ saving on total DNN training energy consumption than the other baselines, while achieving a similar (even better) performance in validation accuracy.