Error Resilient Neuromorphic Networks Using Checker Neurons

Abstract

The last decade has seen tremendous advances in the application of artificial neural networks to solving problems that mimic human intelligence. Many of these systems are implemented using traditional digital compute engines where errors can occur during memory accesses or during numerical computation. While such networks are inherently error resilient, specific errors can result in incorrect decisions. This work develops a low overhead error detection and correction approach for multilayer artificial neural networks, here the hidden layer functions are approximated using checker neurons. Experimental results show that a high coverage of injected errors can be achieved with extremely low computational overhead using consistency properties of the encoded checks. A key side benefit is that the checks can flag errors when the network is presented outlier data that do not correspond to data with which the network is trained to operate.