arXiv subjects

Akash Kumar

Publications and source records attributed to Akash Kumar.

At least 73 records · Page 4Linked to original sources

arXiv2024

Adsorption of Mo and O at S-vacancy on ReS2 surface of ReS2/MoTe2 vdW heterointerface

Applications like high density information storage, neuromorphic computing, nanophotonics, etc. require ultra-thin electronic devices which can be controlled with applied electric field. Of late, atomically thin two-dimensional (2D) materials and van der Waals (vdW) heterointerface of those have emerged as suitable candidates for such ultra-low power nanoelectric devices. In this work, employing density functional theory (DFT), the monolayer ReS2 / monolayer MoTe2 vdW heterostructure with Sulphur vacancy is studied to examine various ground state electronic properties. Changes in effective band gap owing to defect-induced states and modulation of the energy gap value with Molybdenum (Mo) and Oxygen (O) adsorption at the defect site are examined. Since two-dimensional (2D) material based nanoscaled devices exhibit promising switching between non-conducting and conducting states, determining the role of defect-induced states and the adsorption of atoms/molecules on surfaces is crucial. Here, a detailed theoretical study to determine surface properties and relative energetic stability of the vdW heterostructures is carried out. The charge re-distribution between the constituent layers is also analyzed by obtaining Electron Difference Density (EDD) for different heterointerfaces. Nonetheless, the efficacy of switching between non-conducting and conducting states is assessed based on adsorption energy of adatoms binding at the defect site.

cond-mat.mtrl-sci↗

arXiv2024

Ultra-low-current-density single-layer magnetic Weyl semimetal spin Hall nano-oscillators

Topological quantum materials can exhibit unconventional surface states and anomalous transport properties. Still, their applications in spintronic devices are restricted as they require the growth of high-quality thin films with bulk-like properties. Here, we study 10--30 nm thick epitaxial ferromagnetic Co$_{\rm 2}$MnGa films with high structural order and very high values of the anomalous Hall conductivity, $σ_{\rm xy}=1.35\times10^{5}$ $Ω^{-1} m^{-1}$ and the anomalous Hall angle, $θ_{\rm H}=15.8\%$, both comparable to bulk values. We observe a dramatic crystalline orientation dependence of the Gilbert damping constant of a factor of two and a giant intrinsic spin Hall conductivity, $\mathit{σ_{\rm SHC}}=(6.08\pm 0.02)\times 10^{5}$ ($\hbar/2e$) $Ω^{-1} m^{-1}$, an order of magnitude higher than literature values of multilayer Co$_{\rm 2}$MnGa stacks [1-3] and single-layer Ni, Co, Fe [4], and Ni$_{\rm 80}$Fe$_{\rm 20}$~[4,5]. As a consequence, spin-orbit-torque driven auto-oscillations of a 30 nm thick magnetic film are observed for the first time, at an ultralow threshold current density of $J_{th}=6.2\times10^{11}$ $Am^{-2}$. Theoretical calculations of the intrinsic spin Hall conductivity, originating from a strong Berry curvature, corroborate the results and yield values comparable to the experiment. Our results open up for the design of spintronic devices based on single layers of magnetic topological quantum materials.

cond-mat.mes-hall↗

arXiv2024

Spin Hall Nano-Oscillator Empirical Electrical Model for Optimal On-chip Detector Design

As nascent nonlinear oscillators, nano-constriction spin Hall nano-oscillators (SHNOs) represent a promising potential for integration into more complicated systems such as neural networks, magnetic field sensors, and radio frequency (RF) signal classification, their tunable high-frequency operating regime, easy synchronization, and CMOS compatibility can streamline the process. To implement SHNOs in any of these networks, the electrical features of a single device are needed before designing the signal detection CMOS circuitry. This study centers on presenting an empirical electrical model of the SHNO based on a comprehensive characterization of the output impedance of a single SHNO, and its available output power in the range of 2-10 GHz at various bias currents.

cond-mat.mes-hall↗

arXiv2024

Semi-supervised Active Learning for Video Action Detection

In this work, we focus on label efficient learning for video action detection. We develop a novel semi-supervised active learning approach which utilizes both labeled as well as unlabeled data along with informative sample selection for action detection. Video action detection requires spatio-temporal localization along with classification, which poses several challenges for both active learning informative sample selection as well as semi-supervised learning pseudo label generation. First, we propose NoiseAug, a simple augmentation strategy which effectively selects informative samples for video action detection. Next, we propose fft-attention, a novel technique based on high-pass filtering which enables effective utilization of pseudo label for SSL in video action detection by emphasizing on relevant activity region within a video. We evaluate the proposed approach on three different benchmark datasets, UCF-101-24, JHMDB-21, and Youtube-VOS. First, we demonstrate its effectiveness on video action detection where the proposed approach outperforms prior works in semi-supervised and weakly-supervised learning along with several baseline approaches in both UCF101-24 and JHMDB-21. Next, we also show its effectiveness on Youtube-VOS for video object segmentation demonstrating its generalization capability for other dense prediction tasks in videos. The code and models is publicly available at: \url{https://github.com/AKASH2907/semi-sup-active-learning}.

cs.CV↗

arXiv2024

Spintronic devices as next-generation computation accelerators

The ever increasing demand for computational power combined with the predicted plateau for the miniaturization of existing silicon-based technologies has made the search for low power alternatives an industrial and scientifically engaging problem. In this work, we explore spintronics-based Ising machines as hardware computation accelerators. We start by presenting the physical platforms on which this emerging field is being developed, the different control schemes and the type of algorithms and problems on which these machines outperform conventional computers. We then benchmark these technologies and provide an outlook for future developments and use-cases that can help them get a running start for integration into the next generation of computing devices.

cond-mat.mes-hall↗

arXiv2024

Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments

Early Exit Neural Networks (EENNs) present a solution to enhance the efficiency of neural network deployments. However, creating EENNs is challenging and requires specialized domain knowledge, due to the large amount of additional design choices. To address this issue, we propose an automated augmentation flow that focuses on converting an existing model into an EENN. It performs all required design decisions for the deployment to heterogeneous or distributed hardware targets: Our framework constructs the EENN architecture, maps its subgraphs to the hardware targets, and configures its decision mechanism. To the best of our knowledge, it is the first framework that is able to perform all of these steps. We evaluated our approach on a collection of Internet-of-Things and standard image classification use cases. For a speech command detection task, our solution was able to reduce the mean operations per inference by 59.67%. For an ECG classification task, it was able to terminate all samples early, reducing the mean inference energy by 74.9% and computations by 78.3%. On CIFAR-10, our solution was able to achieve up to a 58.75% reduction in computations. The search on a ResNet-152 base model for CIFAR-10 took less than nine hours on a laptop CPU. Our proposed approach enables the creation of EENN optimized for IoT environments and can reduce the inference cost of Deep Learning applications on embedded and fog platforms, while also significantly reducing the search cost - making it more accessible for scientists and engineers in industry and research. The low search cost improves the accessibility of EENNs, with the potential to improve the efficiency of neural networks in a wide range of practical applications.

cs.LG↗

arXiv2024

Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks

Deep Learning is becoming increasingly relevant in Embedded and Internet-of-things applications. However, deploying models on embedded devices poses a challenge due to their resource limitations. This can impact the model's inference accuracy and latency. One potential solution are Early Exit Neural Networks, which adjust model depth dynamically through additional classifiers attached between their hidden layers. However, the real-time termination decision mechanism is critical for the system's efficiency, latency, and sustained accuracy. This paper introduces Difference Detection and Temporal Patience as decision mechanisms for Early Exit Neural Networks. They leverage the temporal correlation present in sensor data streams to efficiently terminate the inference. We evaluate their effectiveness in health monitoring, image classification, and wake-word detection tasks. Our novel contributions were able to reduce the computational footprint compared to established decision mechanisms significantly while maintaining higher accuracy scores. We achieved a reduction of mean operations per inference by up to 80% while maintaining accuracy levels within 5% of the original model. These findings highlight the importance of considering temporal correlation in sensor data to improve the termination decision.

cs.LG↗

arXiv2024

Spin wave-driven variable-phase mutual synchronization in spin Hall nano-oscillators

Spin-orbit torque can drive auto-oscillations of propagating spin wave (PSW) modes in nano-constriction spin Hall nano-oscillators (SHNOs). These modes allow both long-range coupling and the potential of controlling its phase -- critical aspect for nano-magnonics, spin wave logic, and Ising machines. Here, we demonstrate PSW-driven variable-phase coupling between two nano-constriction SHNOs and study how their separation and the PSW wave vector impact their mutual synchronization. In addition to ordinary in-phase mutual synchronization, we observe, using both electrical measurements and phase-resolved $μ-$Brillouin Light Scattering microscopy, mutual synchronization with a phase that can be tuned from 0 to $π$ using the drive current or the applied field. Micromagnetic simulations corroborate the experiments and visualize how the PSW patterns in the bridge connecting the two nano-constrictions govern the coupling. These results advance the capabilities of mutually synchronized SHNOs and open up new possibilities for applications in spin wave logic, unconventional computing, and Ising Machines.

cond-mat.mes-hall↗

arXiv2024

Large Non-Volatile Frequency Tuning of Spin Hall Nano-Oscillators using Circular Memristive Nano-Gates

Spin Hall nano oscillators (SHNOs) are promising candidates for neuromorphic computing due to their miniaturized dimensions, non-linearity, fast dynamics, and ability to synchronize in long chains and arrays. However, tuning the individual SHNOs in large chains/arrays, which is key to implementing synaptic control, has remained a challenge. Here, we demonstrate circular memristive nano-gates, both precisely aligned and shifted with respect to nano-constriction SHNOs of W/CoFeB/HfOx, with increased quality of the device tunability. Gating at the exact center of the nano-constriction region is found to cause irreversible degradation to the oxide layer, resulting in a permanent frequency shift of the auto-oscillating modes. As a remedy, gates shifted outside of the immediate nano-constriction region can tune the frequency dramatically (>200 MHz) without causing any permanent change to the constriction region. Circular memristive nano-gates can, therefore, be used in SHNO chains/arrays to manipulate the synchronization states precisely over large networks of oscillators.

physics.app-ph↗

arXiv2023

A Large-Scale Analysis on Self-Supervised Video Representation Learning

Self-supervised learning is an effective way for label-free model pre-training, especially in the video domain where labeling is expensive. Existing self-supervised works in the video domain use varying experimental setups to demonstrate their effectiveness and comparison across approaches becomes challenging with no standard benchmark. In this work, we first provide a benchmark that enables a comparison of existing approaches on the same ground. Next, we study five different aspects of self-supervised learning important for videos; 1) dataset size, 2) complexity, 3) data distribution, 4) data noise, and, 5)feature analysis. To facilitate this study, we focus on seven different methods along with seven different network architectures and perform an extensive set of experiments on 5 different datasets with an evaluation of two different downstream tasks. We present several interesting insights from this study which span across different properties of pretraining and target datasets, pretext-tasks, and model architectures among others. We further put some of these insights to the real test and propose an approach that requires a limited amount of training data and outperforms existing state-of-the-art approaches which use 10x pretraining data. We believe this work will pave the way for researchers to a better understanding of self-supervised pretext tasks in video representation learning.

cs.CV↗

arXiv2023

AxOMaP: Designing FPGA-based Approximate Arithmetic Operators using Mathematical Programming

With the increasing application of machine learning (ML) algorithms in embedded systems, there is a rising necessity to design low-cost computer arithmetic for these resource-constrained systems. As a result, emerging models of computation, such as approximate and stochastic computing, that leverage the inherent error-resilience of such algorithms are being actively explored for implementing ML inference on resource-constrained systems. Approximate computing (AxC) aims to provide disproportionate gains in the power, performance, and area (PPA) of an application by allowing some level of reduction in its behavioral accuracy (BEHAV). Using approximate operators (AxOs) for computer arithmetic forms one of the more prevalent methods of implementing AxC. AxOs provide the additional scope for finer granularity of optimization, compared to only precision scaling of computer arithmetic. To this end, designing platform-specific and cost-efficient approximate operators forms an important research goal. Recently, multiple works have reported using AI/ML-based approaches for synthesizing novel FPGA-based AxOs. However, most of such works limit usage of AI/ML to designing ML-based surrogate functions used during iterative optimization processes. To this end, we propose a novel data analysis-driven mathematical programming-based approach to synthesizing approximate operators for FPGAs. Specifically, we formulate mixed integer quadratically constrained programs based on the results of correlation analysis of the characterization data and use the solutions to enable a more directed search approach for evolutionary optimization algorithms. Compared to traditional evolutionary algorithms-based optimization, we report up to 21% improvement in the hypervolume, for joint optimization of PPA and BEHAV, in the design of signed 8-bit multipliers.

cs.AR↗

arXiv2023

AxOCS: Scaling FPGA-based Approximate Operators using Configuration Supersampling

The rising usage of AI and ML-based processing across application domains has exacerbated the need for low-cost ML implementation, specifically for resource-constrained embedded systems. To this end, approximate computing, an approach that explores the power, performance, area (PPA), and behavioral accuracy (BEHAV) trade-offs, has emerged as a possible solution for implementing embedded machine learning. Due to the predominance of MAC operations in ML, designing platform-specific approximate arithmetic operators forms one of the major research problems in approximate computing. Recently there has been a rising usage of AI/ML-based design space exploration techniques for implementing approximate operators. However, most of these approaches are limited to using ML-based surrogate functions for predicting the PPA and BEHAV impact of a set of related design decisions. While this approach leverages the regression capabilities of ML methods, it does not exploit the more advanced approaches in ML. To this end, we propose AxOCS, a methodology for designing approximate arithmetic operators through ML-based supersampling. Specifically, we present a method to leverage the correlation of PPA and BEHAV metrics across operators of varying bit-widths for generating larger bit-width operators. The proposed approach involves traversing the relatively smaller design space of smaller bit-width operators and employing its associated Design-PPA-BEHAV relationship to generate initial solutions for metaheuristics-based optimization for larger operators. The experimental evaluation of AxOCS for FPGA-optimized approximate operators shows that the proposed approach significantly improves the quality-resulting hypervolume for multi-objective optimization-of 8x8 signed approximate multipliers.

cs.AR↗

arXiv2023

Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing

Radar sensors offer power-efficient solutions for always-on smart devices, but processing the data streams on resource-constrained embedded platforms remains challenging. This paper presents novel techniques that leverage the temporal correlation present in streaming radar data to enhance the efficiency of Early Exit Neural Networks for Deep Learning inference on embedded devices. These networks add additional classifier branches between the architecture's hidden layers that allow for an early termination of the inference if their result is deemed sufficient enough by an at-runtime decision mechanism. Our methods enable more informed decisions on when to terminate the inference, reducing computational costs while maintaining a minimal loss of accuracy. Our results demonstrate that our techniques save up to 26% of operations per inference over a Single Exit Network and 12% over a confidence-based Early Exit version. Our proposed techniques work on commodity hardware and can be combined with traditional optimizations, making them accessible for resource-constrained embedded platforms commonly used in smart devices. Such efficiency gains enable real-time radar data processing on resource-constrained platforms, allowing for new applications in the context of smart homes, Internet-of-Things, and human-computer interaction.

cs.LG↗

arXiv2023

On the List Color Function Threshold

The chromatic polynomial of a graph $G$, denoted $P(G,m)$, is equal to the number of proper $m$-colorings of $G$. The list color function of graph $G$, denoted $P_{\ell}(G,m)$, is a list analogue of the chromatic polynomial that has been studied since the early 1990s, primarily through comparisons with the corresponding chromatic polynomial. It is known that for any graph $G$ there is a $k \in \mathbb{N}$ such that $P_\ell(G,m) = P(G,m)$ whenever $m \geq k$. The list color function threshold of $G$, denoted $τ(G)$, is the smallest $k \geq χ(G)$ such that $P_{\ell}(G,m) = P(G,m)$ whenever $m \geq k$. In 2009, Thomassen asked whether there is a universal constant $α$ such that for any graph $G$, $τ(G) \leq χ_{\ell}(G) + α$, where $χ_{\ell}(G)$ is the list chromatic number of $G$. We show that the answer to this question is no by proving that there exists a constant $C$ such that $τ(K_{2,l}) - χ_{\ell}(K_{2,l}) \ge C\sqrt{l}$ for $l \ge 16$.

math.CO↗

arXiv2023

Magnetic Proximity induced efficient charge-to-spin conversion in large area PtSe$_{2}$/Ni$_{80}$Fe$_{20}$ heterostructures

As a topological Dirac semimetal with controllable spin-orbit coupling and conductivity, PtSe$_2$, a transition-metal dichalcogenide, is a promising material for several applications from optoelectric to sensors. However, its potential for spintronics applications is yet to be explored. In this work, we demonstrate that PtSe$_{2}$/Ni$_{80}$Fe$_{20}$ heterostructure can generate a large damping-like current-induced spin-orbit torques (SOT), despite the absence of spin-splitting in bulk PtSe$_{2}$. The efficiency of charge-to-spin conversion is found to be $(-0.1 \pm 0.02)$~nm$^{-1}$ in PtSe$_{2}$/Ni$_{80}$Fe$_{20}$, which is three times that of the control sample, Ni$_{80}$Fe$_{20}$/Pt. Our band structure calculations show that the SOT due to the PtSe$_2$ arises from an unexpectedly large spin splitting in the interfacial region of PtSe$_2$ introduced by the proximity magnetic field of the Ni$_{80}$Fe$_{20}$ layer. Our results open up the possibilities of using large-area PtSe$_{2}$ for energy-efficient nanoscale devices by utilizing the proximity-induced SOT.

cond-mat.mtrl-sci↗

arXiv2023

Ultra-low current 10 nm spin Hall nano-oscillators

Nano-constriction based spin Hall nano-oscillators (SHNOs) are at the forefront of spintronics research for emerging technological applications such as oscillator-based neuromorphic computing and Ising Machines. However, their miniaturization to the sub-50 nm width regime results in poor scaling of the threshold current. Here, we show that current shunting through the Si substrate is the origin of this problem and study how different seed layers can mitigate it. We find that an ultra-thin Al$_{2}$O$_{3}$ seed layer and SiN (200 nm) coated p-Si substrates provide the best improvement, enabling us to scale down the SHNO width to a truly nanoscopic dimension of 10 nm, operating at threshold currents below 30 $μ$A. In addition, the combination of electrical insulation and high thermal conductivity of the Al$_{2}$O$_{3}$ seed will offer the best conditions for large SHNO arrays, avoiding any significant temperature gradients within the array. Our state-of-the-art ultra-low operational current SHNOs hence pave an energy-efficient route to scale oscillator-based computing to large dynamical neural networks of linear chains or two-dimensional arrays.

cond-mat.mes-hall↗

arXiv2023

Interfacial origin of unconventional spin-orbit torque in Py/$γ-$IrMn$_{3}$

Angle-resolved spin-torque ferromagnetic resonance measurements are carried out in heterostructures consisting of Py (Ni$_{81}$Fe$_{19}$) and a noncollinear antiferromagnetic quantum material $γ-$IrMn$_{3}$. The structural characterization reveals that $γ-$IrMn$_{3}$ is polycrystalline in nature. A large exchange bias of 158~Oe is found in Py/$γ-$IrMn$_{3}$ at room temperature, while $γ-$IrMn$_{3}$/Py and Py/Cu/$γ-$IrMn$_{3}$ exhibited no exchange bias. Regardless of the exchange bias and stacking sequence, we observe a substantial unconventional out-of-plane anti-damping torque when $γ-$IrMn$_{3}$ is in direct contact with Py. The magnitude of the out-of-plane spin-orbit torque efficiency is found to be twice as large as the in-plane spin-orbit torque efficiency. The unconventional spin-orbit torque vanishes when a Cu spacer is introduced between Py and $γ-$IrMn$_{3}$, indicating that the unconventional spin-orbit torque in this system originates at the interface. These findings are important for realizing efficient antiferromagnet-based spintronic devices via interfacial engineering.

cond-mat.mtrl-sci↗

arXiv2023

Phase noise analysis of mutually synchronized spin Hall nano-oscillators

The reduction of phase noise in electronic systems is of utmost importance in modern communication and signal processing applications and requires an understanding of the underlying physical processes. Here, we systematically study the phase noise in mutually synchronized chains of nano-constriction spin Hall nano-oscillators (SHNOs). We find that longer chains have improved phase noise figures at low offset frequencies (1/f noise), where chains of two and ten mutually synchronized SHNOs have 2.8 and 6.2 dB lower phase noise than single SHNOs. This is close to the theoretical values of 3 and 10 dB, and the deviation is ascribed to process variations between nano-constrictions. However, at higher offset frequencies (thermal noise), the phase noise unexpectedly increases with chain length, which we ascribe to process variations, a higher operating temperature in the long chains at the same drive current and phase delays in the coupling between nano-constrictions.

cond-mat.mes-hall↗