arXiv subjects

Indranil Chakraborty

Publications and source records attributed to Indranil Chakraborty.

At least 19 recordsLinked to original sources

arXiv2026

Integrated cosmological memory: A dark-siren method to probe dark energy

Gravitational-wave (GW) cosmology is currently bottlenecked by the scarcity of electromagnetic counterparts for bright sirens and the systematic uncertainties of galaxy catalogs for dark sirens. We propose a purely gravitational resolution using the Integrated Cosmological Memory (ICM)-the cumulative GW strain encoded in the spacetime geometry of an expanding Universe. While the GW transient emitted by the source provides the luminosity distance, the ICM accumulates a mathematically distinct integral of the cosmic expansion history. We demonstrate that extracting both observables from a single binary merger completely breaks the distance-redshift degeneracy within the gravitational sector. This establishes a novel, catalog-free dark siren framework for third-generation GW detector networks. Crucially, the resulting constraints on late-time dark energy are only weakly sensitive to the local expansion rate, providing a robust cosmological probe that can potentially mitigate the impact of the H0 tension.

astro-ph.CO↗

arXiv2025

Signatures of asymmetry: Gravitational wave memory and the parity violation

Einstein's equivalence principle suggests a deep connection between matter and spacetime, prompting the question: if matter violates parity, must gravity? This letter explores the detection of parity violation in gravity using gravitational wave (GW) memory. Gravitational parity violation could be observable through GW amplitude birefringence and large-scale structure correlations. With improved sensitivity, next-generation GW detectors offer unprecedented opportunities to probe these effects. We propose that the integrated cosmological memory (ICM) of GWs, amplified over cosmological distances, can enhance faint parity-violating signatures. Specifically, if GWs from astrophysical events have differing polarization amplitudes, as in Chern-Simons gravity, ICM significantly amplifies this disparity. ICM uniquely and independently allows us to test fundamental symmetries, constrain gravity parameters, and gain insights into the interplay of particle physics, cosmology and gravity.

gr-qc↗

arXiv2025

A White Paper on The Multi-Messenger Science Landscape in India

The multi-messenger science using different observational windows to the Universe such as Gravitational Waves (GWs), Electromagnetic Waves (EMs), Cosmic Rays (CRs), and Neutrinos offer an opportunity to study from the scale of a neutron star to cosmological scales over a large cosmic time. At the smallest scales, we can explore the structure of the neutron star and the different energetics involved in the transition of a pre-merger neutron star to a post-merger neutron star. This will open up a window to study the properties of matter in extreme conditions and a guaranteed discovery space. On the other hand, at the largest cosmological scales, multi-messenger observations allow us to study the long-standing problems in physical cosmology related to the Hubble constant, dark matter, and dark energy by mapping the expansion history of the Universe using GW sources. Moreover, the multi-messenger studies of astrophysical systems such as white dwarfs, neutron stars, and black holes of different masses, all the way up to a high redshift Universe, will bring insightful understanding into the physical processes associated with them that are inaccessible otherwise. This white paper discusses the key cases in the domain of multi-messenger astronomy and the role of observatories in India which can explore uncharted territories and open discovery spaces in different branches of physics ranging from nuclear physics to astrophysics.

astro-ph.HE↗

arXiv2025

Gravitational radiation from binary systems in Unimodular gravity

Unimodular gravity (UG) is classically considered identical to General Relativity (GR). However, due to restricted diffeomorphism symmetry, the Bianchi identites do not lead to the conservation of energy-momentum tensor. Thus, the conservation of energy-momentum tensor needs to be separately assumed in order to reconcile with GR. Relaxing this assumption, one finds that the conservation violation can lead to differences with GR, which can be subsequently examined in astrophysical and cosmological scenarios. To this end, we examine the predictions of UG in the context of binary systems emitting gravitational radiation. Primarily, we show how the field equations involve a diffusion function which quantifies the measure of non-conservation. Due to this violation, the dispersion relation is modified. Incorporating these changes, we provide an expression for the energy loss by the binaries, which reduces to Peters-Mathews result in the GR limit. Using binary pulsar data, we constrain the theory parameter $ζ$ (which signifies non-conservation) by determining the rate of orbital decay. The strongest constrain on $ζ$ comes out to be $\vert ζ\vert \leq 5\times 10^{-4}$ which is better by an order of magnitude than an existing equivalent constraint coming from the tidal deformability of the neutron stars.

gr-qc↗

arXiv2025

Signatures of gravitational wave memory in the radiative process of entangled quantum probes

In this article, we examine entangled quantum probes in geodesic trajectories in a flat background with a gravitational wave (GW) burst. In particular, these quantum probes are prepared initially either in the symmetric or anti-symmetric Bell's states, and we study the radiative process as the GW burst passes. We split a generic GW burst into two profiles with and without memory. GW burst with (without) memory profiles have different (similar) asymptotic strains between early and late times. We observe that for eternal switching, there is a finite change in the collective atomic transition rate due to the memory part of the GW burst, while the contribution from the without memory counterpart vanishes. We also consider finite Gaussian switching and observe characteristic differences in the radiative process between the GW backgrounds with and without memory. Notably, if the Gaussian switching is peaked much later compared to the passing of GW, only the memory part contributes to the radiative process. Thus, although examined in a simplified set-up, our findings suggest the potential to distinguish bursts with and without GW memory based on the radiative process of entangled detectors.

gr-qc↗

arXiv2024

Gravitational memory signal from neutrino self-interactions in supernova

Neutrinos with large self-interactions, arising from exchange of light scalars or vectors with mass $M_ϕ\simeq 10{\rm MeV}$, can play a useful role in cosmology for structure formation and solving the Hubble tension. It has been proposed that large self-interactions of neutrinos may change the observed properties of supernova like the neutrino luminosity or the duration of the neutrino burst. In this paper, we study the gravitational wave memory signal arising from supernova neutrinos. Our results reveal that memory signal for self-interacting neutrinos are weaker than free-streaming neutrinos in the high frequency range. Implications for detecting and differentiating between such signals for planned space-borne detectors, DECIGO and BBO, are also discussed.

gr-qc↗

arXiv2024

Pruning for Improved ADC Efficiency in Crossbar-based Analog In-memory Accelerators

Deep learning has proved successful in many applications but suffers from high computational demands and requires custom accelerators for deployment. Crossbar-based analog in-memory architectures are attractive for acceleration of deep neural networks (DNN), due to their high data reuse and high efficiency enabled by combining storage and computation in memory. However, they require analog-to-digital converters (ADCs) to communicate crossbar outputs. ADCs consume a significant portion of energy and area of every crossbar processing unit, thus diminishing the potential efficiency benefits. Pruning is a well-studied technique to improve the efficiency of DNNs but requires modifications to be effective for crossbars. In this paper, we motivate crossbar-attuned pruning to target ADC-specific inefficiencies. This is achieved by identifying three key properties (dubbed D.U.B.) that induce sparsity that can be utilized to reduce ADC energy without sacrificing accuracy. The first property ensures that sparsity translates effectively to hardware efficiency by restricting sparsity levels to Discrete powers of 2. The other 2 properties encourage columns in the same crossbar to achieve both Unstructured and Balanced sparsity in order to amortize the accuracy drop. The desired D.U.B. sparsity is then achieved by regularizing the variance of $L_{0}$ norms of neighboring columns within the same crossbar. Our proposed implementation allows it to be directly used in end-to-end gradient-based training. We apply the proposed algorithm to convolutional layers of VGG11 and ResNet18 models, trained on CIFAR-10 and ImageNet datasets, and achieve up to 7.13x and 1.27x improvement, respectively, in ADC energy with less than 1% drop in accuracy.

cs.ET↗

arXiv2024

Prospects for cosmological constraints using gravitational wave memory

The {\Lambda}CDM model has long served as a robust and predictive framework for cosmology, successfully explaining a wide range of observations, including the accelerated expansion of the Universe. However, discrepancies in cosmological parameter estimates and recent findings, such as those from DESI, hint at potential deviations from {\Lambda}CDM. Gravitational wave (GW) observations offer an independent method to probe the nature of dark energy, leveraging GWs from compact binary mergers as standard candles. In this study, we demonstrate that the integrated GW memory over cosmological distances encodes a unique imprint of the background spacetime. Unlike previous analyses, our approach captures non-linear dependencies on cosmological quantities, resulting in an enhancement of the integrated GW memory by a factor of 100 for high-redshift sources well within the sensitivity range of next-generation detectors like Cosmic Explorer and the Einstein Telescope. We find that despite the diminishing strength of individual GWs at high redshifts, their cumulative effect leads to a significant amplification, akin to the integrated Sachs-Wolfe effect, offering a potential new avenue for cosmological studies. By examining a range of dark energy models, we reveal that GW memory is potentially highly sensitive to the underlying cosmological framework, making it a promising probe of dark energy. This novel approach presents the possibility of a fresh perspective to address persistent cosmological tensions, and the nature of dark energy.

gr-qc↗

arXiv2023

WWW: What, When, Where to Compute-in-Memory

Matrix multiplication is the dominant computation during Machine Learning (ML) inference. To efficiently perform such multiplication operations, Compute-in-memory (CiM) paradigms have emerged as a highly energy efficient solution. However, integrating compute in memory poses key questions, such as 1) What type of CiM to use: Given a multitude of CiM design characteristics, determining their suitability from architecture perspective is needed. 2) When to use CiM: ML inference includes workloads with a variety of memory and compute requirements, making it difficult to identify when CiM is more beneficial than standard processing cores. 3) Where to integrate CiM: Each memory level has different bandwidth and capacity, creating different data reuse opportunities for CiM integration. To answer such questions regarding on-chip CiM integration for accelerating ML workloads, we use an analytical architecture-evaluation methodology with tailored mapping algorithm. The mapping algorithm aims to achieve highest weight reuse and reduced data movements for a given CiM prototype and workload. Our analysis considers the integration of CiM prototypes into the cache levels of a tensor-core-like architecture, and shows that CiM integrated memory improves energy efficiency by up to 3.4x and throughput by up to 15.6x compared to established baseline with INT-8 precision. We believe the proposed work provides insights into what type of CiM to use, and when and where to optimally integrate it in the cache hierarchy for efficient matrix multiplication.

cs.AR↗

arXiv2023

Entanglement harvesting for different gravitational wave burst profiles with and without memory

In the present article, we study how different gravitational wave (GW) burst profiles in linearized gravity, with and without the asymptotic memory, may influence the harvesting between two static Unruh-DeWitt detectors. To this end, we investigate the following burst profiles -- Gaussian, sech-squared, Heaviside step function, and tanh. Out of these, the first two bursts contain no memory, while the latter two consist of a non-vanishing memory effect. We find that in all of these cases, entanglement harvesting is possible, and it decreases with the increasing distance between detectors and the detector transition energy. We observe that the harvesting differs qualitatively based on the presence or absence of the memory, which is prominent in a low transition energy regime. With memory, the harvesting keeps increasing with decreasing transition energy, while without memory, it tends to reach finite values. Furthermore, for the two burst profiles without memory, longer bursts correspond to greater harvesting in the low detector transition energy regime, and this characteristic is reversed for larger transition energy. Meanwhile, for the tanh-type profile with memory, harvesting is always greater for shorter bursts. We discuss various implications of our findings.

gr-qc↗

arXiv2023

On the Noise Stability and Robustness of Adversarially Trained Networks on NVM Crossbars

Applications based on Deep Neural Networks (DNNs) have grown exponentially in the past decade. To match their increasing computational needs, several Non-Volatile Memory (NVM) crossbar based accelerators have been proposed. Recently, researchers have shown that apart from improved energy efficiency and performance, such approximate hardware also possess intrinsic robustness for defense against adversarial attacks. Prior works quantified this intrinsic robustness for vanilla DNNs trained on unperturbed inputs. However, adversarial training of DNNs is the benchmark technique for robustness, and sole reliance on intrinsic robustness of the hardware may not be sufficient. In this work, we explore the design of robust DNNs through the amalgamation of adversarial training and intrinsic robustness of NVM crossbar-based analog hardware. First, we study the noise stability of such networks on unperturbed inputs and observe that internal activations of adversarially trained networks have lower Signal-to-Noise Ratio (SNR), and are sensitive to noise compared to vanilla networks. As a result, they suffer on average 2x performance degradation due to the approximate computations on analog hardware. Noise stability analyses show the instability of adversarially trained DNNs. On the other hand, for adversarial images generated using Square Black Box attacks, ResNet-10/20 adversarially trained on CIFAR-10/100 display a robustness gain of 20-30%. For adversarial images generated using Projected-Gradient-Descent (PGD) White-Box attacks, adversarially trained DNNs present a 5-10% gain in robust accuracy due to underlying NVM crossbar when $ε_{attack}$ is greater than $ε_{train}$. Our results indicate that implementing adversarially trained networks on analog hardware requires careful calibration between hardware non-idealities and $ε_{train}$ for optimum robustness and performance.

cs.LG↗

arXiv2022

Gravitational wave memory in wormhole spacetimes

Gravitational wave memory is studied in the context of a certain class of braneworld wormholes. Unlike other wormhole geometries, this novel class of wormholes do not require any exotic matter fields for its traversability. First, we study geodesics in this wormhole spacetime, in the presence of a gravitational wave pulse. The resulting evolution of the geodesic separation shows the presence of displacement and velocity memory effects. Motivated by the same, we study the memory effects at null infinity using the Bondi-Sachs formalism, adapted for braneworld wormhole. Our analysis provides a non-trivial change of the Bondi mass after the passage of a burst of gravitational radiation and hence manifests the memory effect at null infinity. In both of these exercises, the presence of extra dimension and the wormhole nature of the spacetime geometry gets imprinted in the memory effect. Since future gravitational wave detectors will be able to probe the memory effect, the present work provides another avenue to search for compact objects other than black holes.

gr-qc↗

arXiv2022

A simple analytic example of the gravitational wave memory effect

We report an analytical example of the gravitational wave memory effect in exact plane wave spacetimes. A square pulse profile is chosen which gives rise to a curved wave region sandwiched between two flat Minkowski spacetimes. Working in the Brinkmann coordinate system, we solve the geodesic equations exactly in all three regions. Issues related to the continuity and differentiability of the solutions at the boundaries of the pulse are addressed. The evolution of the geodesic separation reveals displacement and velocity memory effects with quantitative estimates depending on initial values and the amplitude, width of the pulse. The deformation caused by the pulse on a ring of particles is then examined in detail. Formation of caustics is found in both scenarios i.e. evolution of separation for a pair of geodesics and shape deformation of a ring of particles -- a feature consistent with previous work on geodesic congruences in this spacetime. In summary, our analysis provides a useful illustration of memory effects involving closed-form exact expressions.

gr-qc↗

arXiv2022

Kundt wave geometries in Eddington-inspired Born-Infeld gravity: New solutions and memory effects

We explore memory effects for novel Kundt wave spacetimes in the recently proposed Eddington-inspired Born-Infeld (EiBI) theory of gravity. First, we construct new, exact Kundt wave geometries in this theory for two different matter sources--(i) generic matter designed to satisfy the field equations as well as energy conditions, and (ii) electromagnetic field. For both sources we find that the EiBI theory parameter $κ$ couples only with the nonradiative part of the physical metric solution. Thereafter, we solve the geodesic and the geodesic deviation equations, in the above spacetimes with the aim of arriving at memory effects. This analysis is carried out numerically and reveals unique memory features depending on the type of matter source present and the signature of the spacetime scalar curvature. The role of $κ$ in influencing the memory effect, for a given background spacetime, is also noted. Thus, apart from providing novel radiative solutions in EiBI gravity, we also show how different matter configurations are responsible for distinct memory characteristics.

gr-qc↗

arXiv2021

Complexity-aware Adaptive Training and Inference for Edge-Cloud Distributed AI Systems

The ubiquitous use of IoT and machine learning applications is creating large amounts of data that require accurate and real-time processing. Although edge-based smart data processing can be enabled by deploying pretrained models, the energy and memory constraints of edge devices necessitate distributed deep learning between the edge and the cloud for complex data. In this paper, we propose a distributed AI system to exploit both the edge and the cloud for training and inference. We propose a new architecture, MEANet, with a main block, an extension block, and an adaptive block for the edge. The inference process can terminate at either the main block, the extension block, or the cloud. The MEANet is trained to categorize inputs into easy/hard/complex classes. The main block identifies instances of easy/hard classes and classifies easy classes with high confidence. Only data with high probabilities of belonging to hard classes would be sent to the extension block for prediction. Further, only if the neural network at the edge shows low confidence in the prediction, the instance is considered complex and sent to the cloud for further processing. The training technique lends to the majority of inference on edge devices while going to the cloud only for a small set of complex jobs, as determined by the edge. The performance of the proposed system is evaluated via extensive experiments using modified models of ResNets and MobileNetV2 on CIFAR-100 and ImageNet datasets. The results show that the proposed distributed model has improved accuracy and energy consumption, indicating its capacity to adapt.

cs.LG↗

arXiv2021

NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems

In-Memory Computing (IMC) hardware using Memristive Crossbar Arrays (MCAs) are gaining popularity to accelerate Deep Neural Networks (DNNs) since it alleviates the "memory wall" problem associated with von-Neumann architecture. The hardware efficiency (energy, latency and area) as well as application accuracy (considering device and circuit non-idealities) of DNNs mapped to such hardware are co-dependent on network parameters, such as kernel size, depth etc. and hardware architecture parameters such as crossbar size. However, co-optimization of both network and hardware parameters presents a challenging search space comprising of different kernel sizes mapped to varying crossbar sizes. To that effect, we propose NAX -- an efficient neural architecture search engine that co-designs neural network and IMC based hardware architecture. NAX explores the aforementioned search space to determine kernel and corresponding crossbar sizes for each DNN layer to achieve optimal tradeoffs between hardware efficiency and application accuracy. Our results from NAX show that the networks have heterogeneous crossbar sizes across different network layers, and achieves optimal hardware efficiency and accuracy considering the non-idealities in crossbars. On CIFAR-10 and Tiny ImageNet, our models achieve 0.8%, 0.2% higher accuracy, and 17%, 4% lower EDAP (energy-delay-area product) compared to a baseline ResNet-20 and ResNet-18 models, respectively.

cs.ET↗

arXiv2021

Kundt geometries and memory effects in the Brans-Dicke theory of gravity

Memory effects are studied in the simplest scalar-tensor theory, the Brans--Dicke (BD) theory. To this end, we introduce, in BD theory, novel Kundt spacetimes (without and with gyratonic terms), which serve as backgrounds for the ensuing analysis on memory. The BD parameter $ω$ and the scalar field ($ϕ$) profile, expectedly, distinguishes between different solutions. Choosing specific localised forms for the free metric functions $H'(u)$ (related to the wave profile) and $J(u)$ (the gyraton) we obtain displacement memory effects using both geodesics and geodesic deviation. An interesting and easy-to-understand exactly solvable case arises when $ω=-2$ (with $J(u)$ absent) which we discuss in detail. For other $ω$ (in the presence of $J$ or without), numerically obtained geodesics lead to results on displacement memory which appear to match qualitatively with those found from a deviation analysis. Thus, the issue of how memory effects in BD theory may arise and also differ from their GR counterparts, is now partially addressed, at least theoretically, within the context of this new class of Kundt geometries.

gr-qc↗

arXiv2021

On the Intrinsic Robustness of NVM Crossbars Against Adversarial Attacks

The increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies. Such NVM crossbars promise fast and energy-efficient in-situ Matrix Vector Multiplication (MVM) thus alleviating the long-standing von Neuman bottleneck in today's digital hardware. However, the analog nature of computing in these crossbars is inherently approximate and results in deviations from ideal output values, which reduces the overall performance of Deep Neural Networks (DNNs) under normal circumstances. In this paper, we study the impact of these non-idealities under adversarial circumstances. We show that the non-ideal behavior of analog computing lowers the effectiveness of adversarial attacks, in both Black-Box and White-Box attack scenarios. In a non-adaptive attack, where the attacker is unaware of the analog hardware, we observe that analog computing offers a varying degree of intrinsic robustness, with a peak adversarial accuracy improvement of 35.34%, 22.69%, and 9.90% for white box PGD (epsilon=1/255, iter=30) for CIFAR-10, CIFAR-100, and ImageNet respectively. We also demonstrate "Hardware-in-Loop" adaptive attacks that circumvent this robustness by utilizing the knowledge of the NVM model.

cs.ET↗