Learn to Learn on Chip: Hardware-aware Meta-learning for Quantized Few-shot Learning at the Edge

Abstract

Recent years have seen a growing trend of deploying deep neural network-based applications on edge devices. Many of these applications, such as biometric identification, activity tracking, user preference learning, etc., require fine-tuning of the trained networks for user personalization. One way to prepare these models to handle new, unseen tasks, is to pretrain them on a distribution of known tasks. This observation has led to increasing research into meta-learning based few-shot learning techniques. However, basic meta-learning approaches do not account for the limited memory and computational resources during on-chip training. We propose a modified metalearning algorithm that enables quantized fine-tuning to optimally condition the models for on-chip few shot learning. The modification involves the inclusion of target hardware constraints upfront in the meta-learning process. Block floating point datatypes with low precision mantissa bits are utilized in the forward and backward passes, to allow hardware-friendly adaptation. Experiments show that our algorithm provides better initializations than conventional algorithms, more suitable for efficient quantized fine-tuning. This allows the few-shot learner to achieve better convergence, in terms of accuracy and speed. Extensive experiments are also performed to analyze the impact of initialization on quantized fine-tuning and further corroborate the benefits of our method.