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Emerging resistive-crossbar memory (RCM) technology can be promising for computationally-expensive analog pattern-matching tasks. However, the use of CMOS analog-circuits with RCM would result in large power-consumption and poor scalability, thereby eschewing the benefits of RCM-based computation. We propose the use of low-voltage, fast-switching,(More)
While the promise of spin-torque devices for future on-chip memory is now well recognized, application of spin devices in computational hardware remains an exploratory research-domain. Several 'all-spin' as well as hybrid design-techniques have been explored for computing applications of nano-magnets. A majority of such efforts have been focused on digital(More)
Recent years have witnessed growing interest in the field of brain-inspired computing based on neural-network architectures. In order to translate the related algorithmic models into powerful, yet energy-efficient cognitive-computing hardware, computing-devices beyond CMOS may need to be explored. The suitability of such devices to this field of computing(More)
Hierarchical temporal memory (HTM) tries to mimic the computing in cerebral neocortex. It identifies spatial and temporal patterns in the input for making inferences. This may require a large number of computationally expensive tasks, such as dot product evaluations. Nanodevices that can provide direct mapping for such primitives are of great interest. In(More)
In this paper a new approach of reducing power for a given system is developed that is self resetting logic, a parallel compressor is developed for multiplier by reducing its power with facilitation of this low power logic technique. By using this technique the power dissipation is significantly reduced with respect to other logics. By implementing the(More)
As CMOS technology begins to face significant scaling challenges, considerable research efforts are being directed to investigate alternative device technologies that can serve as a replacement for CMOS. Spintronic devices, which utilize the spin of electrons as the state variable for computation, have recently emerged as one of the leading candidates for(More)
Recent years have witnessed growing interest in the use of Artificial Neural Networks (ANNs) for vision, classification, and inference problems. An artificial neuron sums N weighted inputs and passes the result through a non-linear transfer function. Large-scale ANNs impose very high computing requirements for training and classification, leading to great(More)
In this paper we discuss the potential of emerging spin-torque devices for computing applications. Recent proposals for spin-based computing schemes may be differentiated as all-spin? vs. hybrid, programmable vs. fixed, and, Boolean vs. non-Boolean. All-spin logic-styles may offer high area-density due to small form-factor of nano-magnetic devices. However,(More)