Bhaswar Chakrabarti

Shubham Pande, Karthi Srinivasan, Suresh Balanethiram et al.

The event-driven nature of spiking neural networks makes them biologically plausible and more energy-efficient than artificial neural networks. In this work, we demonstrate motion detection of an object in a two-dimensional visual field. The network architecture presented here is biologically plausible and uses CMOS analog leaky integrate-and-fire neurons and ultra-low power multi-layer RRAM synapses. Detailed transistorlevel SPICE simulations show that the proposed structure can accurately and reliably detect complex motions of an object in a two-dimensional visual field.

F. Merrikh Bayat, M. Prezioso, B. Chakrabarti et al.

We experimentally demonstrate classification of 4x4 binary images into 4 classes, using a 3-layer mixed-signal neuromorphic network ("MLP perceptron"), based on two passive 20x20 memristive crossbar arrays, board-integrated with discrete CMOS components. The network features 10 hidden-layer and 4 output-layer analog CMOS neurons and 428 metal-oxide memristors, i.e. is almost an order of magnitude more complex than any previously reported functional memristor circuit. Moreover, the inference operation of this classifier is performed entirely in the integrated hardware. To deal with larger crossbar arrays, we have developed a semi-automatic approach to their forming and testing, and compared several memristor training schemes for coping with imperfect behavior of these devices, as well as with variability of analog CMOS neurons. The effectiveness of the proposed schemes for defect and variation tolerance was verified experimentally using the implemented network and, additionally, by modeling the operation of a larger network, with 300 hidden-layer neurons, on the MNIST benchmark. Finally, we propose a simple modification of the implemented memristor-based vector-by-matrix multiplier to allow its operation in a wider temperature range.