Though deep neural network models exhibit outstanding performance for various
applications, their large model size and extensive floating-point operations
render deployment on mobile computing platforms a major challenge, and, in
particular, on Internet of Things devices. One appealing solution is model
quantization that reduces the model size and uses integer operations commonly
supported by microcontrollers . To this end, a 1-bit quantized DNN model or
deep binary neural network maximizes the memory efficiency, where each
parameter in a BNN model has only 1-bit. In this paper, we propose a
reconfigurable BNN (RBNN) to further amplify the memory efficiency for
resource-constrained IoT devices. Generally, the RBNN can be reconfigured on
demand to achieve any one of M (M>1) distinct tasks with the same parameter
set, thus only a single task determines the memory requirements. In other
words, the memory utilization is improved by times M. Our extensive experiments
corroborate that up to seven commonly used tasks can co-exist (the value of M
can be larger). These tasks with a varying number of classes have no or
negligible accuracy drop-off on three binarized popular DNN architectures
including VGG, ResNet, and ReActNet. The tasks span across different domains,
e.g., computer vision and audio domains validated herein, with the prerequisite
that the model architecture can serve those cross-domain tasks. To protect the
intellectual property of an RBNN model, the reconfiguration can be controlled
by both a user key and a device-unique root key generated by the intrinsic
hardware fingerprint. By doing so, an RBNN model can only be used per paid user
per authorized device, thus benefiting both the user and the model provider.

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