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Enabling On-device Continual Learning with Binary Neural Networks
On-device learning remains a formidable challenge, especially when dealing with resource-constrained devices that have limited computational capabilities.
On-Device Learning with Binary Neural Networks
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.
Input Layer Binarization with Bit-Plane Encoding
Binary Neural Networks (BNNs) use 1-bit weights and activations to efficiently execute deep convolutional neural networks on edge devices.
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