Learning with Local Gradients at the Edge

AI-based systems operating in rapidly changing environments need to adapt in realtime. The adaptability of the current edge devices is limited by Size, Weight and Power (SWaP) constraints, the memory wall from current system architectures [1], and the lack of efficient on-device learning algorithms. As a result, current AI systems adapt by retraining on the cloud due to the overwhelming complexity of AI models. Adaptation, or domain transfer for a new data distribution, often requires fine-tuning of the entire or partial network (e.g., last few layers). Similarly, it is often necessary to perform conversion from full precision [2] to fewer bits in order to fit the limited hardware architecture, and doing so might require updating the network by distillation.