Still searching Arxiv, refresh for possibly better results.

total 3914took 0.16s

Stimulating STDP to Exploit Locality for Lifelong Learning without Catastrophic ForgettingFeb 08 2019Stochastic gradient descent requires that training samples be drawn from a uniformly random distribution of the data. For a deployed system that must learn online from an uncontrolled and unknown environment, the ordering of input samples often fails ... More

Investigation of Dependence between Time-zero and Time-dependent Variability in High-k NMOS TransistorsAug 17 2016Bias Temperature Instability (BTI) is a major reliability concern in CMOS technology, especially with High dielectric constant (High-\k{appa}/HK) metal gate (MG) transistors. In addition, the time independent process induced variation has also increased ... More

Discretization based Solutions for Secure Machine Learning against Adversarial AttacksFeb 08 2019Feb 11 2019Adversarial examples are perturbed inputs that are designed (from a deep learning network's (DLN) parameter gradients) to mislead the DLN during test time. Intuitively, constraining the dimensionality of inputs or parameters of a network reduces the 'space' ... More

Modeling and Analysis of Loading Effect in Leakage of Nano-Scaled Bulk-CMOS Logic CircuitsOct 25 2007In nanometer scaled CMOS devices significant increase in the subthreshold, the gate and the reverse biased junction band-to-band-tunneling (BTBT) leakage, results in the large increase of total leakage power in a logic circuit. Leakage components interact ... More

Localization of Dirac-like excitations in graphene in the presence of smooth inhomogeneous magnetic fieldsAug 09 2011Dec 14 2011The present article discusses magnetic confinement of the Dirac excitations in graphene in presence of inhomogeneous magnetic fields. In the first case a magnetic field directed along the z axis whose magnitude is proportional to $1/r$ is chosen. In the ... More

Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the EdgeFeb 01 2019The recent advent of `Internet of Things' (IOT) has increased the demand for enabling AI-based edge computing. This has necessitated the search for efficient implementations of neural networks in terms of both computations and storage. Although extreme ... More

I-Min: An Intelligent Fermat Point Based Energy Efficient Geographic Packet Forwarding Technique for Wireless Sensor and Ad Hoc NetworksJun 18 2010Energy consumption and delay incurred in packet delivery are the two important metrics for measuring the performance of geographic routing protocols for Wireless Adhoc and Sensor Networks (WASN). A protocol capable of ensuring both lesser energy consumption ... More

Toward Fast Neural Computing using All-Photonic Phase Change Spiking NeuronsApr 01 2018Aug 28 2018The rapid growth of brain-inspired computing coupled with the inefficiencies in the CMOS implementations of neuromrphic systems has led to intense exploration of efficient hardware implementations of the functional units of the brain, namely, neurons ... More

8T SRAM Cell as a Multi-bit Dot Product Engine for Beyond von-Neumann ComputingFeb 22 2018Oct 16 2018Large scale digital computing almost exclusively relies on the von-Neumann architecture which comprises of separate units for storage and computations. The energy expensive transfer of data from the memory units to the computing cores results in the well-known ... More

Buttiker Probe Based Modeling of TDDB: Application to Dielectric Breakdown in MTJs and MOS DevicesAug 13 2016Feb 24 2017Dielectric layers are gradually being down-scaled in different electronic devices like MOSFETs and Magnetic Tunnel Junctions (MTJ) with shrinking device sizes. As a result, time dependent dielectric breakdown (TDDB) has become a major issue in such devices. ... More

Hierarchical Temporal Memory Based on Spin-Neurons and Resistive Memory for Energy-Efficient Brain-Inspired ComputingFeb 10 2014Hierarchical 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 large number of computationally expensive tasks like, dot-product evaluations. ... More

Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute ArraysJul 01 2018Oct 22 2018Deep neural networks are a biologically-inspired class of algorithms that have recently demonstrated state-of-the-art accuracies involving large-scale classification and recognition tasks. Indeed, a major landmark that enables efficient hardware accelerators ... More

Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object RecognitionFeb 03 2016We present a spike-based unsupervised regenerative learning scheme to train Spiking Deep Networks (SpikeCNN) for object recognition problems using biologically realistic leaky integrate-and-fire neurons. The training methodology is based on the Auto-Encoder ... More

ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic ComputingFeb 11 2019In this work, we propose ReStoCNet, a residual stochastic multilayer convolutional Spiking Neural Network (SNN) composed of binary kernels, to reduce the synaptic memory footprint and enhance the computational efficiency of SNNs for complex pattern recognition ... More

Spintronic Switches for Ultra Low Energy On-Chip and Inter-Chip Current-Mode InterconnectsApr 08 2013Apr 19 2013Energy-efficiency and design-complexity of high-speed on-chip and inter-chip data-interconnects has emerged as the major bottleneck for high-performance computing-systems. As a solution, we propose an ultra-low energy interconnect design-scheme using ... More

Short-Term Plasticity and Long-Term Potentiation in Magnetic Tunnel Junctions: Towards Volatile SynapsesOct 31 2015Feb 01 2016Synaptic memory is considered to be the main element responsible for learning and cognition in humans. Although traditionally non-volatile long-term plasticity changes have been implemented in nanoelectronic synapses for neuromorphic applications, recent ... More

Exploring Ultra Low-Power on-Chip Clocking Using Functionality Enhanced Spin-Torque SwitchesDec 30 2013Emerging spin-torque (ST) phenomena may lead to ultra-low-voltage, high-speed nano-magnetic switches. Such current-based-switches can be attractive for designing low swing global-interconnects, like, clocking-networks and databuses. In this work we present ... More

Attention Tree: Learning Hierarchies of Visual Features for Large-Scale Image RecognitionAug 01 2016One of the key challenges in machine learning is to design a computationally efficient multi-class classifier while maintaining the output accuracy and performance. In this paper, we present a tree-based classifier: Attention Tree (ATree) for large-scale ... More

Image Edge Detection based on Swarm Intelligence using Memristive NetworksJun 16 2016Recent advancements in the development of memristive devices has opened new opportunities for hardware implementation of non-Boolean computing. To this end, the suitability of memristive devices for swarm intelligence algorithms has enabled researchers ... More

MESL: Proposal for a Non-volatile Cascadable Magneto-Electric Spin LogicNov 23 2016In the quest for novel, scalable and energy-efficient computing technologies, many non-charge based logic devices are being explored. Recent advances in multi-ferroic materials have paved the way for electric field induced low energy and fast switching ... More

Theory of phonon-modified quantum dot photoluminescence intensity in structured photonic reservoirsNov 21 2014Apr 10 2015The spontaneous emission rate of a quantum dot coupled to a structured photonic reservoir is determined by the frequency dependence of its local density of photon states. Through phonon-dressing, a breakdown of Fermi's golden rule can occur for certain ... More

Spontaneous emission from a quantum dot in a structured photonic reservoir: phonon-mediated breakdown of Fermi's golden ruleJun 13 2014Jun 01 2015We describe how a structured photonic medium controls the spontaneous emission rate from an excited quantum dot in the presence of electron-phonon coupling. We analyze this problem using a polaron transformed master equation and we consider specific examples ... More

Quantum theory of light emission from quantum dots coupled to structured photonic reservoirs and acoustic phononsApr 13 2015Electron-phonon coupling in semiconductor quantum dots plays a significant role in determining the optical properties of excited excitons, especially the spectral nature of emitted photons. This paper presents a comprehensive theory and analysis of emission ... More

Technology Aware Training in Memristive Neuromorphic Systems based on non-ideal Synaptic CrossbarsNov 24 2017The advances in the field of machine learning using neuromorphic systems have paved the pathway for extensive research on possibilities of hardware implementations of neural networks. Various memristive technologies such as oxide-based devices, spintronics ... More

Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental LearningFeb 15 2018May 23 2018In recent years, Convolutional Neural Networks (CNNs) have shown remarkable performance in many computer vision tasks such as object recognition and detection. However, complex training issues, such as `catastrophic forgetting' and hyper-parameter tuning, ... More

Uniqueness of positive solution for a quasilinear elliptic equation in heisenberg groupDec 09 2015In this article we are interested in addressing the question of existence and uniqueness of positive solution to a quasilinear elliptic equation involving p-laplacian in Heisenberg Group. The idea is to prove the uniqueness by using Diaz-Saa Inequality ... More

A structural and a functional aspect of stable information processing by the brainJan 21 2007Jun 16 2007In this paper a model of neural circuit in the brain has been proposed which is composed of cyclic sub-circuits. A big loop has been defined to be consisting of a feed forward path from the sensory neurons to the highest processing area of the brain and ... More

A new measure of phase synchronization for a pair of time series and seizure focus localizationDec 13 2006Dec 22 2006Defining and measuring phase synchronization in a pair of nonlinear time series are highly nontrivial. This can be done with the help of Fourier transform, when it exists, for a pair of stored (hence stationary) signals. In a time series instantaneous ... More

On Gauge Invariance and Covariant Derivatives in Metric SpacesFeb 07 2017Feb 04 2019In this manuscript, we will discuss the construction of covariant derivative operator in quantum gravity. We will find it is appropriate to use affine connections more general than metric compatible connections in quantum gravity. We will demonstrate ... More

Behavioral response to strong aversive stimuli: A neurodynamical modelApr 04 2007In this paper a theoretical model of functioning of a neural circuit during a behavioral response has been proposed. A neural circuit can be thought of as a directed multigraph whose each vertex is a neuron and each edge is a synapse. It has been assumed ... More

An FFT based measure of phase synchronizationDec 04 2006Apr 25 2008In this paper phase of a signal has been viewed from a different angle. According to this view a signal can have countably infinitely many phases, one associated with each Fourier component. In other words each frequency has a phase associated with it. ... More

Closed Intersecting Families of finite sets and their applicationsNov 06 2014Dec 06 2014Paul Erd\H{o}s and L\'aszl\'o Lov\'asz established that any \emph{maximal intersecting family of $k-$sets} has at most $k^{k}$ blocks. They introduced the problem of finding the maximum possible number of blocks in such a family. They also showed that ... More

Exploiting Challenges of Sub-20 nm CMOS for Affordable Technology ScalingSep 02 2015For the past four decades, cost and features have driven CMOS scaling. Severe lithography and material limitations seen below the 20 nm node, however, are challenging the fundamental premise of affordable CMOS scaling. Just continuing to co-optimize leaf ... More

Ising spin model using Spin-Hall Effect (SHE) induced magnetization reversal in Magnetic-Tunnel-JunctionSep 19 2016Sep 25 2016Ising spin model is considered as an efficient computing method to solve combinatorial optimization problems based on its natural tendency of convergence towards low energy state. The underlying basic functions facilitating the Ising model can be categorized ... More

Some intricacies of the momentum operator in quantum mechanicsJun 06 2007Sep 23 2008In quantum mechanics textbooks the momentum operator is defined in the Cartesian coordinates and rarely the form of the momentum operator in spherical polar coordinates is discussed. Consequently one always generalizes the Cartesian prescription to other ... More

Ultra-low Energy, High-Performance Dynamic Resistive Threshold LogicAug 08 2013We propose dynamic resistive threshold-logic (DRTL) design based on non-volatile resistive memory. A threshold logic gate (TLG) performs summation of multiple inputs multiplied by a fixed set of weights and compares the sum with a threshold. DRTL employs ... More

Ultra Low Power Associative Computing with Spin Neurons and Resistive Crossbar MemoryApr 08 2013Emerging 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 ... More

A Photonic In-Memory Computing primitive for Spiking Neural Networks using Phase-Change MaterialsAug 03 2018Oct 24 2018Spiking Neural Networks (SNNs) offer an event-driven and more biologically realistic alternative to standard Artificial Neural Networks based on analog information processing. This can potentially enable energy-efficient hardware implementations of neuromorphic ... More

Multiple alignment of structures using center of proteinsDec 28 2014In this paper we report on an algorithm for aligning multiple protein structures. The algorithm has been tested on a variety of inputs and it performs well in comparison to well-known algorithms for this problem.

Spin Neurons: A Possible Path to Energy-Efficient Neuromorphic ComputersSep 12 2013Recent 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 ... More

Large magnetoelectric coupling in nanoscale BiFeO$_3$ from direct electrical measurementsSep 20 2014We report the results of direct measurement of remanent hysteresis loops on nanochains of BiFeO$_3$ at room temperature under zero and $\sim$20 kOe magnetic field. We noticed a suppression of remanent polarization by nearly $\sim$40\% under the magnetic ... More

Discretization based Solutions for Secure Machine Learning against Adversarial AttacksFeb 08 2019Adversarial examples are perturbed inputs that are designed (from a deep learning network's (DLN) parameter gradients) to mislead the DLN during test time. Intuitively, constraining the dimensionality of inputs or parameters of a network reduces the 'space' ... More

Hybrid Spintronic-CMOS Spiking Neural Network With On-Chip Learning: Devices, Circuits and SystemsOct 01 2015Nov 13 2015Over the past decade Spiking Neural Networks (SNN) have emerged as one of the popular architectures to emulate the brain. In SNN, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, ... More

Conditional Deep Learning for Energy-Efficient and Enhanced Pattern RecognitionSep 29 2015Jan 28 2016Deep learning neural networks have emerged as one of the most powerful classification tools for vision related applications. However, the computational and energy requirements associated with such deep nets can be quite high, and hence their energy-efficient ... More

DSTT-MRAM: Differential Spin Hall MRAM for On-chip MemoriesMay 17 2013A new device structure for spin transfer torque based magnetic random access memory is proposed for on-chip memory applications. Our device structure exploits spin Hall effect to create a differential memory cell that exhibits fast and energy-efficient ... More

Polaron master equation theory of pulse driven phonon-assisted population inversion and single photon emission from quantum dot excitonsDec 24 2015Jan 07 2016We introduce an intuitive and semi-analytical polaron master equation approach to model pulse-driven population inversion and emitted single photons from a quantum dot exciton. The master equation theory allows one to identify important phonon-induced ... More

Hybrid Spintronic-CMOS Spiking Neural Network With On-Chip Learning: Devices, Circuits and SystemsOct 01 2015Nov 04 2016Over the past decade Spiking Neural Networks (SNN) have emerged as one of the popular architectures to emulate the brain. In SNN, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, ... More

Design and Synthesis of Ultra Low Energy Spin-Memristor Threshold LogicFeb 10 2014A threshold logic gate (TLG) performs weighted sum of multiple inputs and compares the sum with a threshold. We propose Spin-Memeristor Threshold Logic (SMTL) gates, which employ memristive cross-bar array (MCA) to perform current-mode summation of binary ... More

PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning InferenceJan 29 2019Jan 30 2019Memristor crossbars are circuits capable of performing analog matrix-vector multiplications, overcoming the fundamental energy efficiency limitations of digital logic. They have been shown to be effective in special-purpose accelerators for a limited ... More

Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in FerromagnetsOct 02 2015Feb 03 2016Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, ... More

Boolean and Non-Boolean Computation With Spin DevicesApr 19 2013Aug 22 2013Recently several device and circuit design techniques have been explored for applying nano-magnets and spin torque devices like spin valves and domain wall magnets in computational hardware. However, most of them have been focused on digital logic, and, ... More

Ultra-low Energy, High Performance and Programmable Magnetic Threshold LogicAug 08 2013We propose magnetic threshold-logic (MTL) design based on non-volatile spin-torque switches. A threshold logic gate (TLG) performs summation of multiple inputs multiplied by a fixed set of weights and compares the sum with a threshold. MTL employs resistive ... More

Energy-Efficient Memories using Magneto-Electric Switching of FerromagnetsJan 27 2017Voltage driven magneto-electric (ME) switching of ferro-magnets has shown potential for future low-energy spintronic memories. In this paper, we first analyze two different ME devices viz. ME-MTJ and ME-XNOR device with respect to writability, readability ... More

Design of a Low Voltage Analog-to-Digital Converter using Voltage Controlled Stochastic Switching of Low Barrier NanomagnetsMar 04 2018May 23 2018The inherent stochasticity in many nano-scale devices makes them prospective candidates for low-power computations. Such devices have been demonstrated to exhibit probabilistic switching between two stable states to achieve stochastic behavior. Recently, ... More

Proposal for a Leaky-Integrate-Fire Spiking Neuron based on Magneto-Electric Switching of Ferro-magnetsSep 29 2016The efficiency of the human brain in performing classification tasks has attracted considerable research interest in brain-inspired neuromorphic computing. Hardware implementations of a neuromorphic system aims to mimic the computations in the brain through ... More

A Mathematical model for Astrocytes mediated LTP at Single Hippocampal SynapsesJul 26 2011Mar 12 2012Many contemporary studies have shown that astrocytes play a significant role in modulating both short and long form of synaptic plasticity. There are very few experimental models which elucidate the role of astrocyte over Long-term Potentiation (LTP). ... More

On the gaps between non-zero Fourier coefficients of eigenforms with CMAug 15 2016Suppose $E$ is an elliptic curve over $\mathbb{Q}$ of conductor $N$ with complex multiplication (CM) by $\mathbb{Q}(i)$, and $f_E$ is the corresponding cuspidal Hecke eigenform in $S^{\mathrm{new}}_2(\Gamma_0(N))$. Then $n$-th Fourier coefficient of $f_E$ ... More

A Mathematical Model of Tripartite Synapse: Astrocyte Induced Synaptic PlasticityMay 04 2011Mar 12 2012In this paper we present a biologically detailed mathematical model of tripartite synapses, where astrocytes modulate short-term synaptic plasticity. The model consists of a pre-synaptic bouton, a post-synaptic dendritic spine-head, a synaptic cleft and ... More

Microstructure-enabled control of wrinkling in nematic elastomer sheetsNov 25 2016Nematic elastomers are rubbery solids which have liquid crystals incorporated into their polymer chains. These materials display many unusual mechanical properties, one such being the ability to from fine-scale microstructure. In this work, we explore ... More

On the Universality and Extremality of graphs with a distance constrained colouringJan 04 2019A lambda colouring (or $L(2,1)-$colouring) of a graph is an assignment of non-negative integers (with minimum assignment $0$) to its vertices such that the adjacent vertices must receive integers at least two apart and vertices at distance two must receive ... More

Ground State Properties of Neutron Magic NucleiAug 16 2016A systematic study of the ground state properties of the entire chains of even even neutron magic nuclei represented by isotones of traditional neutron magic numbers N = 8, 20, 40, 50, 82 and 126 has been carried out using relativistic mean field (rmf) ... More

Separation of Dirac equation in the 3+1 dimensional constant curvature black hole background and its solutionJan 25 2008The behavior of spin-half particles is discussed in the 3 + 1-dimensional constant curvature black hole (CCBH) spacetime. We use Schwarzschild-like coordinates, valid outside the black hole event horizon. The constant time surfaces corresponding to the ... More

Learning Gaussian Mixtures with Arbitrary SeparationJul 06 2009May 13 2010In this paper we present a method for learning the parameters of a mixture of $k$ identical spherical Gaussians in $n$-dimensional space with an arbitrarily small separation between the components. Our algorithm is polynomial in all parameters other than ... More

A Geometric Analysis of Time Series Leading to Information Encoding and a New Entropy MeasureOct 13 2018A time series is uniquely represented by its geometric shape, which also carries information. A time series can be modelled as the trajectory of a particle moving in a force field with one degree of freedom. The force acting on the particle shapes the ... More

Performance Analysis of CSMA/CA based Medium Access in Full Duplex Wireless CommunicationsDec 13 2015Full duplex communication promises a paradigm shift in wireless networks by allowing simultaneous packet transmission and reception within the same channel. While recent prototypes indicate the feasibility of this concept, there is a lack of rigorous ... More

Capacitively Driven Global Interconnect with Magnetoelectric Switching Based Receiver for Higher Energy EfficiencyFeb 26 2018We propose capacitively driven low-swing global interconnect circuit using a receiver that utilizes magnetoelectric (ME) effect induced magnetization switching to reduce the energy consumption. Capacitively driven wire has recently been shown to be effective ... More

Energy-Efficient Object Detection using Semantic DecompositionSep 29 2015Sep 20 2016Machine-learning algorithms offer immense possibilities in the development of several cognitive applications. In fact, large scale machine-learning classifiers now represent the state-of-the-art in a wide range of object detection/classification problems. ... More

Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking NeuronsOct 01 2015Jul 23 2016Brain-inspired computing architectures attempt to mimic the computations performed in the neurons and the synapses in the human brain in order to achieve its efficiency in learning and cognitive tasks. In this work, we demonstrate the mapping of the probabilistic ... More

Ultra Low Energy Analog Image Processing Using Spin NeuronsJun 12 2012Apr 08 2013In this work we present an ultra low energy, 'on-sensor' image processing architecture, based on cellular array of spin based neurons. The 'neuron' constitutes of a lateral spin valve (LSV) with multiple input magnets, connected to an output magnet, using ... More

Energy-Efficient and Robust Associative Computing with Electrically Coupled Dual Pillar Spin-Torque OscillatorsSep 12 2013Dynamics of coupled spin-torque oscillators can be exploited for non-Boolean information processing. However, the feasibility of coupling large number of STOs with energy-efficiency and sufficient robustness towards parameter-variation and thermal-noise, ... More

ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural NetworksMar 22 2017Jun 08 2018A fundamental feature of learning in animals is the "ability to forget" that allows an organism to perceive, model and make decisions from disparate streams of information and adapt to changing environments. Against this backdrop, we present a novel unsupervised ... More

Thermoelectric Spin-Transfer Torque MRAM with Sub-Nanosecond Bi-Directional Writing using Magnonic CurrentAug 11 2011A new genre of Spin-Transfer Torque (STT) MRAM is proposed, in which bi-directional writing is achieved using thermoelectrically controlled magnonic current as an alternative to conventional electric current. The device uses a magnetic tunnel junction ... More

Buttiker Probe Based Modeling of TDDB: Application to Dielectric Breakdown in MTJs and MOS DevicesAug 13 2016Dielectric layers are gradually being down-scaled in different electronic devices like MOSFETs and Magnetic Tunnel Junctions (MTJ) with shrinking device sizes. As a result, time dependent dielectric breakdown (TDDB) has become a major issue in such devices. ... More

FALCON: Feature Driven Selective Classification for Energy-Efficient Image RecognitionSep 12 2016Machine-learning algorithms have shown outstanding image recognition or classification performance for computer vision applications. However, the compute and energy requirement for implementing such classifier models for large-scale problems is quite ... More

Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel JunctionMay 15 2016Deep Spiking Neural Networks are becoming increasingly powerful tools for cognitive computing platforms. However, most of the existing literature on such computing models are developed with limited insights on the underlying hardware implementation, resulting ... More

Spin-Torque Sensors for Energy Efficient High Speed Long InterconnectsDec 02 2015In this paper, we propose a Spin-Torque (ST) based sensing scheme that can enable energy efficient multi-bit long distance interconnect architectures. Current-mode interconnects have recently been proposed to overcome the performance degradations associated ... More

Exploring Boolean and Non-Boolean Computing Applications of Spin Torque DevicesAug 13 2013Aug 16 2013In this paper we discuss the potential of emerging spintorque devices for computing applications. Recent proposals for spinbased computing schemes may be differentiated as all-spin vs. hybrid, programmable vs. fixed, and, Boolean vs. non-Boolean. All ... More

Statistical Modeling of Pipeline Delay and Design of Pipeline under Process Variation to Enhance Yield in sub-100nm TechnologiesOct 25 2007Operating frequency of a pipelined circuit is determined by the delay of the slowest pipeline stage. However, under statistical delay variation in sub-100nm technology regime, the slowest stage is not readily identifiable and the estimation of the pipeline ... More

Modeling and Simulation of Spin Transfer Torque Generated at Topological Insulator/Ferromagnetic HeterostructureJun 26 2015Jul 29 2015Topological Insulator (TI) has recently emerged as an attractive candidate for possible application to spintronic circuits because of its strong spin orbit coupling. TIs are unique materials that have an insulating bulk but conducting surface states due ... More

Proposal For Neuromorphic Hardware Using Spin DevicesJun 14 2012Jul 18 2012We present a design-scheme for ultra-low power neuromorphic hardware using emerging spin-devices. We propose device models for 'neuron', based on lateral spin valves and domain wall magnets that can operate at ultra-low terminal voltage of ~20 mV, resulting ... More

Spin-Based Neuron Model with Domain Wall Magnets as SynapseMay 28 2012Aug 15 2012We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and domain wall ... More

Spin Orbit Torque Based Electronic NeuronOct 06 2014A device based on current-induced spin-orbit torque (SOT) that functions as an electronic neuron is proposed in this work. The SOT device implements an artificial neuron's thresholding (transfer) function. In the first step of a two-step switching scheme, ... More

Exploring Spin-Transfer-Torque Devices for Logic ApplicationsDec 30 2014Mar 23 2015As CMOS nears the end of the projected scaling roadmap, significant effort has been devoted to the search for new materials and devices that can realize memory and logic. Spintronics, is one of the promising directions for the Post-CMOS era. While the ... More

Yield, Area and Energy Optimization in Stt-MRAMs using failure aware ECCSep 28 2015Jun 16 2016Spin Transfer Torque MRAMs are attractive due to their non-volatility, high density and zero leakage. However, STT-MRAMs suffer from poor reliability due to shared read and write paths. Additionally, conflicting requirements for data retention and write-ability ... More

STT-SNN: A Spin-Transfer-Torque Based Soft-Limiting Non-Linear Neuron for Low-Power Artificial Neural NetworksDec 23 2014Recent 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. ... More

Perovskite Quantum OrganismoidsMar 03 2017A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making1-3. This behavior, known as habituation, is universal among forms of life with a central nervous system, ... More

Spin-Orbit Torque Induced Spike-Timing Dependent PlasticityDec 19 2014Nanoelectronic devices that mimic the functionality of synapses are a crucial requirement for performing cortical simulations of the brain. In this work we propose a ferromagnet-heavy metal heterostructure that employs spin-orbit torque to implement Spike-Timing ... More

Reality of linear and angular momentum expectation values in bound statesApr 03 2007In quantum mechanics textbooks the momentum operator is defined in the Cartesian coordinates and rarely the form of the momentum operator in spherical polar coordinates is discussed. Consequently one always generalizes the Cartesian prescription to other ... More

Resonance fluorescence spectra from semiconductor quantum dots coupled to slow-light photonic crystal waveguidesMar 09 2016Using a polaron master equation approach we investigate the resonance fluorescence spectra from coherently driven quantum dots (QDs) coupled to an acoustic phonon bath and a photonic crystal waveguide with a rich local density of photon states (LDOS). ... More

Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural ComputingFeb 27 2016Large-scale artificial neural networks have shown significant promise in addressing a wide range of classification and recognition applications. However, their large computational requirements stretch the capabilities of computing platforms. The fundamental ... More

Going Deeper in Spiking Neural Networks: VGG and Residual ArchitecturesFeb 07 2018Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware. However, their application in machine learning have largely been limited to very shallow neural network ... More

On the Reversible Geodesics for a Finsler space with Randers change of Quartic metricDec 22 2018In this paper, we consider a Finsler space with a Randers change of Quartic metric F = $\sqrt[4]{\alpha^4 + \beta^4} + \beta$. The conditions for this space to be with reversible geodesics are obtained. Further, we study some geometrical properties of ... More

Laser Induced Magnetization Reversal for Detection in Optical InterconnectsOct 09 2014Optical interconnect has emerged as the front-runner to replace electrical interconnect especially for off-chip communication. However, a major drawback with optical interconnects is the need for photodetectors and amplifiers at the receiver, implemented ... More

Computable g- FramesOct 26 2016The notion of g-frames for Hilbert spaces was introduced and studied by Wenchang Sun [16] as a generalization of the notion of frames. In this paper, we define computable g-frames in computable Hilbert spaces and obtain computable versions of some of ... More

Complexity Analysis of Reversible Logic SynthesisFeb 03 2014Jun 24 2014Reversible logic circuit is a necessary construction for achieving ultra low power dissipation as well as for prominent post-CMOS computing technologies such as Quantum computing. Consequently automatic synthesis of a Boolean function using elementary ... More

Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural NetworksFeb 27 2016Multilayered artificial neural networks (ANN) have found widespread utility in classification and recognition applications. The scale and complexity of such networks together with the inadequacies of general purpose computing platforms have led to a significant ... More

Magnonic spin-transfer torque MRAM with low power, high speed, and error-free switchingMay 27 2011A new class of spin-transfer torque magnetic random access memory (STT-MRAM) is discussed, in which writing is achieved using thermally initiated magnonic current pulses as an alternative to conventional electric current pulses. The magnonic pulses are ... More

Energy Efficient and High Performance Current-Mode Neural Network Circuit using Memristors and Digitally Assisted Analog CMOS NeuronsNov 29 2015Dec 01 2015Emerging nano-scale programmable Resistive-RAM (RRAM) has been identified as a promising technology for implementing brain-inspired computing hardware. Several neural network architectures, that essentially involve computation of scalar products between ... More

Time dependent nonclassical properties of even and odd nonlinear coherent statesSep 21 1999We construct even and odd nonlinear coherent states of a parametric oscillator and examine their nonclassical properties.It has been shown that these superpositions exhibit squeezing and photon antibunching which change with time.

Ultra-High Density, High-Performance and Energy-Efficient All Spin LogicAug 10 2013All Spin Logic gates employ multiple nano-magnets interacting through spin-torque using non-magnetic channels. Compactness, non-volatility and ultra-low voltage operation are some of the attractive features of ASL, while, low switching-speed (of nano-magnets ... More