Advancing Memristive Analog Neuromorphic Networks: Increasing Complexity, and Coping with Imperfect Hardware Components
This work addresses the problem of scaling and reliability in neuromorphic computing hardware for researchers and engineers, representing an incremental advance with specific experimental gains.
The authors tackled the challenge of building more complex and robust memristive neuromorphic hardware by experimentally demonstrating a 3-layer network with 428 memristors, classifying 4x4 binary images into 4 classes entirely in hardware, which is nearly 10 times more complex than prior functional circuits. They developed training schemes to handle device imperfections and variability, validated on their network and via MNIST modeling, and proposed a temperature-tolerant modification.
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.