Memristor based object detection using neural network

With the increasing growth of AI, big data analytics, cloud computing, and Internet of Things applications, developing memristor devices and related hardware systems to compute the deep learning application needs extensive data calculations with low power consumption and lesser chip area. Deep learning model is one of the AI methods which is gaining importance in object detection, natural language processing, and pattern recognition. A large amount of data handling is essential for driving the deep learning model with less power consumption. To address these issues, the paper proposed the Memristor-based object detection on the CIFAR-10 dataset and achieved an accuracy of 85 percent. The memtorch package in python is employed to predict the objects for implementation.