Abstract
In this paper, we report our studies on integrated active pixel sensor (APS) array and memristor crossbar neural network to perform image learning and recognition in an unsupervised fashion. APS modules encode light intensity/gray value of grayscale images into APS sensing current feeding into N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ×M crossbar array. The memristor is used as a synapse and can be trained through an adaption of spike timing dependent plasticity (STDP). After training, different images are stored into different post-synaptic neuron dendrites. In the image recognition stage, a simple pulse counter circuit was used to check the matched image. System level simulations show that the network can store grayscale image correctly and perform image recognition in a simple and efficient way.
Published Version
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