SelfChecking Residue Number System for LowPower Reliable Neural Network Neural Network suffers four major issues including acceleration, power consumption, area overhead and fault tolerance. In this paper we develop a systematic approach to design a lowpower, compact, fast and reliable neural network based on a redundant residue number system. Residue number systems have been applied in designing neural network except the CORDICbased activation functions including hypertangent, logistic and softmax functions. This issue results in that the entire neural network cannot be totally selfchecked and extra operations make the time, power and area reductions wasted. In our systematic approach we propose some design rules for ensuring the checking rate without loss of reductions in time, area and power consumption. From experiments on three neural network with 24bit fixedpoint operations for the MNIST handwritten digit data set, 3, 4, and 5 moduli are separately employed for achieving all balanced improvements in powersaving, areareduction, speedacceleration and reliability promotion. Experimental results show that all the power, time and area can be reduced to only about one third, and the entire network in any combination of software and hardware can be selfchecked in an aliasing rate of only 0.39% and TMRcorrectable under the singleresidue fault model.

SanityCheck: Boosting the Reliability of SafetyCritical Deep Neural Network Applications The widespread usage of deep neural networks in autonomous driving necessitates a consideration of the safety arguments against hardwarelevel faults. This study confirms the possible catastrophic impact of hardwarelevel faults on DNN accuracy; the consequent need for lowcost fault tolerance methods can be met through a rigorous exploration of the mathematical properties of the associated computations. We propose SanityCheck, which makes use of the linearity property and employs spatial and temporal checksums to protect fullyconnected and convolutional layers in deep neural networks. SanityCheck can be purely implemented on software and deployed on different execution platforms with no additional modification. We also propose SanityCheck hardware which integrates seamlessly with modern DNN accelerators and neutralizes the small performance overhead in pure software implementations. SanityCheck delivers perfect errorcaused misprediction coverage in our experiments, which makes it a promising candidate for boosting the reliability of safetycritical deep neural network applications.