RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things

Though deep neural network (DNN) models exhibit outstanding performance for various applications, their large model size and extensive floating-point operations (FLOPS) render deployment on mobile computing platforms a major challenge, and, in particular, on Internet of Things (IoT) devices. One appealing solution is model quantization that reduces the model size and uses integer operations commonly supported by microcontrollers (MCUs usually do not support FLOPS). To this end, a 1-bit quantized DNN model or deep binary neural network (BNN) 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 ×M . Our extensive experiments corroborate that up to seven commonly used tasks (M = 7, 6 of these tasks are image related and the last one is audio) 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 (i.e., within 1%) 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 (IP) 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 (e.g., SRAM memory power-up pattern). 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.