On-Device Learning with Binary Neural Networks
This work addresses the problem of deploying deep learning models on resource-constrained devices for applications requiring continuous adaptation, representing an incremental advancement in continual learning techniques.
The paper tackles the challenge of enabling continual learning on low-power embedded devices by proposing a hybrid quantization method for Binary Neural Networks, achieving efficient on-device learning with minimal latency overhead.
Existing Continual Learning (CL) solutions only partially address the constraints on power, memory and computation of the deep learning models when deployed on low-power embedded CPUs. In this paper, we propose a CL solution that embraces the recent advancements in CL field and the efficiency of the Binary Neural Networks (BNN), that use 1-bit for weights and activations to efficiently execute deep learning models. We propose a hybrid quantization of CWR* (an effective CL approach) that considers differently forward and backward pass in order to retain more precision during gradient update step and at the same time minimizing the latency overhead. The choice of a binary network as backbone is essential to meet the constraints of low power devices and, to the best of authors' knowledge, this is the first attempt to prove on-device learning with BNN. The experimental validation carried out confirms the validity and the suitability of the proposed method.