A Configurable and Fully Synthesizable RTL-Based Convolutional Neural Network for Biosensor Applications

This paper presents a register-transistor level (RTL) based convolutional neural network (CNN) for biosensor applications. Biosensor-based diseases detection by DNA identification using biosensors is currently needed. We proposed a synthesizable RTL-based CNN architecture for this purpose. The adopted technique of parallel computation of multiplication and accumulation (MAC) approach optimizes the hardware overhead by significantly reducing the arithmetic calculation and achieves instant results. While multiplier bank sharing throughout the convolutional operation with fully connected operation significantly reduces the implementation area. The CNN model is trained in MATLAB^® on MNIST^® handwritten dataset. For validation, the image pixel array from MNIST^® handwritten dataset is applied on proposed RTL-based CNN architecture for biosensor applications in ModelSim^®. The consistency is checked with multiple test samples and 92% accuracy is achieved. The proposed idea is implemented in 28 nm CMOS technology. It occupies 9.986 mm² of the total area. The power requirement is 2.93 W from 1.8 V supply. The total time taken is 8.6538 ms.