In-Situ AI: Towards Autonomous and Incremental Deep Learning for IoT Systems

Abstract

Recent years have seen an exploration of data volumes from a myriad of IoT devices, such as various sensors and ubiquitous cameras. The deluge of IoT data creates enormous opportunities for us to explore the physical world, especially with the help of deep learning techniques. Traditionally, the Cloud is the option for deploying deep learning based applications. However, the challenges of Cloud-centric IoT systems are increasing due to significant data movement overhead, escalating energy needs, and privacy issues. Rather than constantly moving a tremendous amount of raw data to the Cloud, it would be beneficial to leverage the emerging powerful IoT devices to perform the inference task. Nevertheless, the statically trained model could not efficiently handle the dynamic data in the real in-situ environments, which leads to low accuracy. Moreover, the big raw IoT data challenges the traditional supervised training method in the Cloud. To tackle the above challenges, we propose In-situ AI, the first Autonomous and Incremental computing framework and architecture for deep learning based IoT applications. We equip deep learning based IoT system with autonomous IoT data diagnosis (minimize data movement), and incremental and unsupervised training method (tackle the big raw IoT data generated in ever-changing in-situ environments). To provide efficient architectural support for this new computing paradigm, we first characterize the two In-situ AI tasks (i.e. inference and diagnosis tasks) on two popular IoT devices (i.e. mobile GPU and FPGA) and explore the design space and tradeoffs. Based on the characterization results, we propose two working modes for the In-situ AI tasks, including Single-running and Co-running modes. Moreover, we craft analytical models for these two modes to guide the best configuration selection. We also develop a novel two-level weight shared In-situ AI architecture to efficiently deploy In-situ tasks to IoT node. Compared with traditional IoT systems, our In-situ AI can reduce data movement by 28-71%, which further yields 1.4X-3.3X speedup on model update and contributes to 30-70% energy saving.