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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. 

In this research, a low cost error detection and correction approach is developed for multilayer perceptron networks, where checker neurons are used to encode hidden layer functions using independent training experiments. Error detection and correction is predicated on validating consistency properties of the encoded checks and shows that high coverage of injected errors can be achieved with extremely low computational overhead. 

The last decade has seen tremendous advances in the transformation of ubiquitous control, computing and communication platforms that are anytime, anywhere. These platforms allow humans to interact with machines through sensing, control and actuation functions in ways not imaginable a few decades ago. While robust control techniques aim to maintain autonomous system performance in the presence of bounded modeling errors, they are not designed to manage large multiparameter variations and internal component failures that are inevitable during lengthy periods of field deployment. To address the trustworthiness of autonomous systems in the field, we propose a cross-layer error resilience approach in which errors are detected and corrected at appropriate levels of the design (hardware-through software) with the objective of minimizing the latency of error recovery while maintaining high failure coverage. At the control processor level, soft errors in the digital control processor are considered. At the system level, sensor and actuator failures are analyzed. These impairments define the health of the system. A methodology for adapting the control procedure of the autonomous system to compensate for degraded system health is proposed. It is shown how this methodology can be applied to simple linear and nonlinear control systems to maintain system performance in the presence of internal component failures. Experimental results demonstrate the feasibility of the proposed methodology.