Synthesis of Approximate Logic on Memristive Crossbars The realization of extremely low power compact devices is critical for ubiquitous computing. In that context, approximate computing is one approach towards improving computing efficiency in errortolerant applications that may endure a small loss in accuracy in exchange for better efficiency. In this paper, we present a framework for automated synthesis of crossbar designs that implement approximate versions of compute kernels using flowbased computing. We propose Approximate Free Binary Decision Diagrams (AFBDDs) using a relaxed function equality criterion in node merging. We create AFBDD representations of kernels which are then mapped onto crossbars. These crossbars can compute an approximation of the original compute kernel. Our framework allows us to easily tune the accuracyefficiency tradeoff for an approximate design. We generate designs for RevLib and MCNC benchmarks, and basic image processing kernels. Our designs are up to 80% more compact than exact designs while retaining xe2x89x88 90% accuracy.

DefectTolerant and EnergyEfficient Training of MultiValued and Binary Memristor Crossbars for NearSensor Cognitive Computing Nearsensor cognitive computing can reduce an amount of data to be delivered to the server significantly. A memristorCMOS hybrid circuit can be integrated with IoT sensors, for the nearsensor cognitive computing. One problem of the real memristor crossbar is memristor defects. In this paper, we discuss the chipintheloop learning to consider the memristor defects in the crossbar. For energyefficient and defecttolerant training of the crossbar, we explain the partialgated training for the multivalued memristors. Moreover, for binarymemristor neural networks, we show the asymmetrical programming scheme of the combination of coarseHRS and fineLRS can save the programming time and energy much than the highprecision programming of the entire crossbar.