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Results for "Kaushik Roy"

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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
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
Computing in Memory with Spin-Transfer Torque Magnetic RAMMar 06 2017Nov 21 2017In-memory computing is a promising approach to addressing the processor-memory data transfer bottleneck in computing systems. We propose Spin-Transfer Torque Compute-in-Memory (STT-CiM), a design for in-memory computing with Spin-Transfer Torque Magnetic ... More
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive CrossbarsAug 31 2018Jan 18 2019Resistive crossbars have emerged as promising building blocks for realizing DNNs due to their ability to compactly and efficiently realize the dominant DNN computational kernel, viz., vector-matrix multiplication. However, a key challenge with resistive ... More
Optical Receiver with Helicity Dependent Switching of MagnetizationFeb 21 2018In this work, we propose helicity-dependent switching (HDS) of magnetization of Co/Pt for energy efficient optical receiver. Designing a low power optical receiver for optical-to-electrical signal conversion has proven to be very challenging. Current ... More
Chaos-guided Input Structuring for Improved Learning in Recurrent Neural NetworksDec 26 2017Feb 18 2018Anatomical studies demonstrate that brain reformats input information to generate reliable responses for performing computations. However, it remains unclear how neural circuits encode complex spatio-temporal patterns. We show that neural dynamics are ... More
Encoding Neural and Synaptic Functionalities in Electron Spin: A Pathway to Efficient Neuromorphic ComputingNov 07 2017Dec 20 2017Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform everyday. This has recently resulted in a seismic shift in the field of computation where ... More
Image Edge Detection based on Swarm Intelligence using Memristive NetworksJun 16 2016Feb 14 2017Recent 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
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
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
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
Non-equilibrium Green's Function and First Principle Approach to Modeling of Multiferroic Tunnel JunctionsJun 17 2019Recently, multiferroic tunnel junctions (MFTJs) have gained significant spotlight in the literature due to its high tunneling electro-resistance together with its non-volatility. In order to analyze such devices and to have insightful understanding of ... More
Improving Photoplethysmographic Measurements under Motion Artifacts using Artificial Neural Network for Personal HealthcareJul 14 2018Photoplethysmographic (PPG) measurements are susceptible to motion artifacts (MA) due to movement of the peripheral body parts. In this paper, we present a new approach to identify the MA corrupted PPG beats and then rectify the beat morphology 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
Inclusive prompt photon production in electron-nucleus scattering at small xFeb 26 2018We compute the differential cross-section for inclusive prompt photon production in deeply inelastic scattering (DIS) of electrons on nuclei at small $x$ in the framework of the Color Glass Condensate (CGC) effective theory. The leading order (LO) computation ... More
Short-Term Plasticity and Long-Term Potentiation in Magnetic Tunnel Junctions: Towards Volatile SynapsesOct 31 2015Dec 19 2017Synaptic 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
Implicit Generative Modeling of Random Noise during Training for Adversarial RobustnessJul 05 2018Feb 08 2019We introduce a Noise-based prior Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the implicit generative modeling of random noise with the same loss function used during posterior ... 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
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
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
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
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
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
Magnetic Skyrmions for Cache MemoryMay 01 2017Magnetic skyrmions (MS) are particle-like spin structures with whirling configuration, which are promising candidates for spin-based memory. MS contains alluring features including remarkably high stability, ultra low driving current density, and compact ... 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
Synthesizing Images from Spatio-Temporal Representations using Spike-based BackpropagationMay 24 2019Spiking neural networks (SNNs) offer a promising alternative to current artificial neural networks to enable low-power event-driven neuromorphic hardware. Spike-based neuromorphic applications require processing and extracting meaningful information from ... 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
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
A Few Comments on Classical ElectrodynamicsMay 08 2006Mar 25 2017In this article we will discuss a few aspects of the spacetime description of fields and particles. In Section:I we will demonstrate that a line is not just a collection of points and we will have to introduce one-dimensional line intervals as fundamental ... 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
Near-Horizon Geometry and the Entropy of a Minimally Coupled Scalar Field in the Kerr Black HoleJun 18 2016Jul 25 2016In this article we will discuss a Lorentzian sector calculation of the entropy of a minimally coupled scalar field in the Kerr black hole background. We will use the brick wall model of t' Hooft. In the Kerr black hole, complications arise due to the ... 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
Enabling Spike-based Backpropagation in State-of-the-art Deep Neural Network ArchitecturesMar 15 2019Mar 25 2019Spiking Neural Networks (SNNs) has recently emerged as a prominent neural computing paradigm. However, the typical shallow spiking network architectures have limited capacity for expressing complex representations, while training a very deep spiking network ... 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
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
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
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
STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient RecognitionOct 12 2017Spiking Neural Networks (SNNs) with a large number of weights and varied weight distribution can be difficult to implement in emerging in-memory computing hardware due to the limitations on crossbar size (implementing dot product), the constrained number ... 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
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
SPARE: Spiking Networks Acceleration Using CMOS ROM-Embedded RAM as an In-Memory-Computation PrimitiveNov 20 2017Jul 31 2018Despite huge success of artificial intelligence, hardware systems running these algorithms consume orders of magnitude higher energy compared to the human brain, mainly due to heavy data movements between the memory unit and the computation cores. Spiking ... More
TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system designAug 26 2017Mar 04 2018Implementation of Neuromorphic Systems using post Complementary Metal-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged as a promising solution to enable low-power acceleration of neural networks. However, the recent ... More
A Low Effort Approach to Structured CNN Design Using PCADec 15 2018Mar 12 2019Deep learning models hold state of the art performance in many fields, yet their design is still based on heuristics or grid search methods. This work proposes a method to analyze a trained network and deduce an optimized, compressed architecture that ... 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
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
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
X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural NetworksJun 29 2019There is widespread interest in emerging technologies, especially resistive crossbars for accelerating Deep Neural Networks (DNNs). Resistive crossbars offer a highly-parallel and efficient matrix-vector-multiplication (MVM) operation. MVM being the most ... 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
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network SharingDec 07 2017Sep 17 2018Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification. However, retraining these large networks to accommodate new, previously unseen data demands high computational time ... 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
Enabling Spike-based Backpropagation in State-of-the-art Deep Neural Network ArchitecturesMar 15 2019Spiking Neural Networks (SNNs) has recently emerged as a prominent neural computing paradigm. However, the typical shallow spiking network architectures have limited capacity for expressing complex representations, while training a very deep spiking network ... More
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural NetworksMar 10 2017Mar 20 2017Brain-inspired learning models attempt to mimic the cortical architecture and computations performed in the neurons and synapses constituting the human brain to achieve its efficiency in cognitive tasks. In this work, we present convolutional spike timing ... 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
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
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
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
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
A Low Effort Approach to Structured CNN Design Using PCADec 15 2018Deep learning models hold state of the art performance in many fields, yet their design is still based on heuristics or grid search methods. This work proposes a method to analyze a trained network and deduce a redundancy-free, compressed architecture ... More
Evaluating the Stability of Recurrent Neural Models during Training with Eigenvalue Spectra AnalysisMay 08 2019We analyze the stability of recurrent networks, specifically, reservoir computing models during training by evaluating the eigenvalue spectra of the reservoir dynamics. To circumvent the instability arising in examining a closed loop reservoir system ... More
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network SharingDec 07 2017May 02 2019Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification. However, retraining these large networks to accommodate new, previously unseen data demands high computational time ... More
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural NetworksMay 12 2017Convolutional Neural Networks (CNN) are being increasingly used in computer vision for a wide range of classification and recognition problems. However, training these large networks demands high computational time and energy requirements; hence, their ... More
Reinforcement Learning with Low-Complexity Liquid State MachinesJun 04 2019We propose reinforcement learning on simple networks consisting of random connections of spiking neurons (both recurrent and feed-forward) that can learn complex tasks with very little trainable parameters. Such sparse and randomly interconnected recurrent ... More
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
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.
PCA-driven Hybrid network design for enabling Intelligence at the EdgeJun 04 2019The recent advent of IOT has increased the demand for enabling AI-based edge computing in several applications including healthcare monitoring systems, autonomous vehicles etc. This has necessitated the search for efficient implementations of neural networks ... More
Possible potentials responsible for stable circular relativistic orbitsMar 17 2011Bertrand's theorem in classical mechanics of the central force fields attracts us because of its predictive power. It categorically proves that there can only be two types of forces which can produce stable, circular orbits. In the present article an ... More
Non-existence results for the weighted $p$-Laplace equation with singular nonlinearitiesDec 20 2017In this paper we present some non existence results concerning the stable solutions to the equation $$\operatorname{div}(w(x)|\nabla u|^{p-2}\nabla u)=g(x)f(u)\;\;\mbox{in}\;\;\mathbb{R}^N;\;\;p\geq 2$$ when $f(u)$ is either $u^{-\delta}+u^{-\gamma}$, ... More
A note on the transversal size of a series of families constructed over Cycle GraphJan 09 2015Paul Erd\H{o}s and L\'{a}szl\'{o} Lov\'{a}sz established by means of an example that there exists a maximal intersecting family of $k-$sets with approximately $(e-1)k!$ blocks. L\'{a}szl\'{o} Lov\'{a}sz conjectured that their example is best known example ... More
Intricacies of Cosmological bounce in polynomial metric $f(R)$ gravity for flat FLRW spacetimeSep 06 2015Jan 13 2016In this paper we present the techniques for computing cosmological bounces in polynomial $f(R)$ theories, whose order is more than two, for spatially flat FLRW spacetime. In these cases the conformally connected Einstein frame shows up multiple scalar ... More
Unsupervised Single Image Underwater Depth EstimationMay 25 2019May 28 2019Depth estimation from a single underwater image is one of the most challenging problems and is highly ill-posed. Due to the absence of large generalized underwater depth datasets and the difficulty in obtaining ground truth depth-maps, supervised learning ... 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
Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking NeuronsOct 01 2015Dec 19 2017Brain-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
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
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
Stochastic Spiking Neural Networks Enabled by Magnetic Tunnel Junctions: From Nontelegraphic to Telegraphic Switching RegimesSep 26 2017Jan 26 2018Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-effcient cognitive intelligence. The computational model attempt to exploit the intrinsic device stochasticity ... 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
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
FALCON: Feature Driven Selective Classification for Energy-Efficient Image RecognitionSep 12 2016Mar 08 2017Machine-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
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
Stochastic Spin-Orbit Torque Devices as Elements for Bayesian InferenceJul 15 2017Sep 11 2017Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact, preliminary research has revealed that stochastic functionalities also ... 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
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
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
Going Deeper in Spiking Neural Networks: VGG and Residual ArchitecturesFeb 07 2018Feb 19 2019Over 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
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
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
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
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
PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator DesignJun 11 2019The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, especially for edge devices. Owing ... More