BIC: Bio-inspired Information Processing Using Hybrid Nanodevice Arrays

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(i) Technical Description: Preliminary studies indicate that hybrid semiconductor/molecular ("CMOL") circuits may serve as a basis for neuromorphic network arrays ("CrossNets") capable of advanced mixed-signal information processing with unprecedented density (beyond 1012 active devices per cm2) and performance (up to 1020 elementary operations per second per cm2) at acceptable power consumption (below 100 W/cm2). The objective of this project is to the key issue of the CMOL CrossNets: the development of effective techniques of their training (including the error backpropagation and global reinforcement), and the demonstration of the latter technique of such an important task as modeling attention. (ii) Non-technical Explanation: Preliminary estimates indicate that array circuits, based on both semiconductor (CMOS) devices and nanoelectronic (e.g., molecular) devices and using bio-inspired architectures, may provide advanced information processing with much higher performance than the usual semiconductor Boolean logic circuits. However, in order to enable such processing, the existing methods of neural network training should be modified to accommodate the limitations imposed by the hybrid array hardware. This project focuses on the development of two most prospective training techniques, and their demonstration for modeling such a key function of brain as attention.