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.

翻译：现有持续学习方法仅部分解决了低功耗嵌入式CPU上部署深度学习模型时对功率、内存和计算的约束。本文提出一种持续学习方法，融合了持续学习领域的最新进展与二进制神经网络（使用1位表示权重和激活值以高效执行深度学习模型）的高效性。我们提出了一种CWR*（一种有效的持续学习方法）的混合量化方案，该方案对前向传播和反向传播进行差异化处理，以便在梯度更新步骤中保留更多精度，同时最小化延迟开销。选择二进制网络作为主干网络对于满足低功耗设备的约束至关重要，据作者所知，这是首次尝试证明基于二进制神经网络的设备端学习可行性。实验验证结果证实了所提方法的有效性与适用性。