arXiv subjects

Tamal Ghosh

Publications and source records attributed to Tamal Ghosh.

12 recordsLinked to original sources

arXiv2026

Architectural Design and Performance Analysis of FPGA based AI Accelerators: A Comprehensive Review

Deep learning (DL) has emerged as a rapidly developing advanced technology, enabling the performance of complex tasks involving image recognition, natural language processing, and autonomous decision-making with high levels of accuracy. However, as these technologies evolve and strive to meet the growing demands of real-life applications, the complexity of DL models continues to increase. These models require processing of massive volumes of data, demanding substantial computational power and memory bandwidth. This gives rise to the critical need for hardware accelerators that can deliver both high performance and energy efficiency. Accelerator types include ASIC based solutions, GPU accelerators, and FPGA based implementations. The limitations of ASIC and GPU accelerators have led to FPGAs becoming one of the prominent solutions, offering distinct advantages for DL workloads. FPGAs provide a flexible and reconfigurable platform, allowing model specific customization while maintaining high efficiency. This article explores various hardware level optimizations for DL. These optimizations include techniques such as loop pipelining, parallelism, quantization, and various memory hierarchy enhancements. In addition, it provides an overview of state-of-the-art FPGA-based neural network accelerators. Through the study and analysis of these accelerators, several challenges have been identified, paving the way for future optimizations and innovations in the design of FPGA-based hardware accelerators.

cs.AR↗

arXiv2025

Bio-Inspired Neuron Synapse Optimization for Adaptive Learning and Smart Decision-Making

Purpose: Optimization challenges in science, engineering, and real-world applications often involve complex, high-dimensional, and multimodal search spaces. Traditional optimization methods frequently struggle with local optima entrapment, slow convergence, and inefficiency in large-scale environments. This study aims to address these limitations by proposing a novel optimization algorithm inspired by neural mechanisms. Design/methodology/approach: The paper introduces Neuron Synapse Optimization (NSO), a new metaheuristic algorithm inspired by neural interactions. NSO features key innovations such as fitness-based synaptic weight updates to improve search influence, adaptive pruning to minimize computational overhead, and dual guidance from global and local best solutions to balance exploration and exploitation. The algorithm was benchmarked against popular metaheuristics and the recently published Hippopotamus Optimization Algorithm (HOA) using the CEC 2014 test suite, encompassing unimodal, multimodal, and composition function landscapes. Findings: Benchmark results reveal that NSO consistently outperforms HOA and other major algorithms in terms of convergence speed, robustness, and scalability. NSO demonstrates superior adaptability and efficiency, particularly in complex, high-dimensional search spaces. Originality: NSO introduces a unique blend of neural-inspired mechanisms with dynamic resource allocation, setting it apart from existing algorithms. Its innovative design enhances search performance while reducing computational cost. With promising applications in technology, healthcare, data science, and engineering, NSO paves the way for future research into dynamic and multi-objective optimization, machine learning hyperparameter tuning, and real-world engineering design problems.

cs.NE↗

arXiv2025

Hybrid Deep Learning Framework for Enhanced Diabetic Retinopathy Detection: Integrating Traditional Features with AI-driven Insights

Diabetic Retinopathy (DR), a vision-threatening complication of Dia-betes Mellitus (DM), is a major global concern, particularly in India, which has one of the highest diabetic populations. Prolonged hyperglycemia damages reti-nal microvasculature, leading to DR symptoms like microaneurysms, hemor-rhages, and fluid leakage, which, if undetected, cause irreversible vision loss. Therefore, early screening is crucial as DR is asymptomatic in its initial stages. Fundus imaging aids precise diagnosis by detecting subtle retinal lesions. This paper introduces a hybrid diagnostic framework combining traditional feature extraction and deep learning (DL) to enhance DR detection. While handcrafted features capture key clinical markers, DL automates hierarchical pattern recog-nition, improving early diagnosis. The model synergizes interpretable clinical data with learned features, surpassing standalone DL approaches that demon-strate superior classification and reduce false negatives. This multimodal AI-driven approach enables scalable, accurate DR screening, crucial for diabetes-burdened regions.

cs.CV↗

arXiv2025

Comparative Analysis of Object Detection Algorithms for Surface Defect Detection

This article compares the performance of six prominent object detection algorithms, YOLOv11, RetinaNet, Fast R-CNN, YOLOv8, RT-DETR, and DETR, on the NEU-DET surface defect detection dataset, comprising images representing various metal surface defects, a crucial application in industrial quality control. Each model's performance was assessed regarding detection accuracy, speed, and robustness across different defect types such as scratches, inclusions, and rolled-in scales. YOLOv11, a state-of-the-art real-time object detection algorithm, demonstrated superior performance compared to the other methods, achieving a remarkable 70% higher accuracy on average. This improvement can be attributed to YOLOv11s enhanced feature extraction capabilities and ability to process the entire image in a single forward pass, making it faster and more efficient in detecting minor surface defects. Additionally, YOLOv11's architecture optimizations, such as improved anchor box generation and deeper convolutional layers, contributed to more precise localization of defects. In conclusion, YOLOv11's outstanding performance in accuracy and speed solidifies its position as the most effective model for surface defect detection on the NEU dataset, surpassing competing algorithms by a substantial margin.

cs.CV↗

arXiv2023

Tensile quantum-to-classical transition of macroscopic entangled states under complete coarse-grained measurements

The macroscopic limit at which the quantum-to-classical transition occurs remains as one of the long-standing questions in the foundations of quantum theory. There are evidences that the macroscopic limit to which the quantumness of a system persists depends on the degree of interaction due to the measurement processes. For instance, with a system having a considerably large Hilbert space dimension, if the measurement is performed in such a way that the outcome of the measurement only reveals a coarse-grained version of the information about the individual level of the concerned system then the disturbance due to the measurement process can be considered to be infinitesimally small. Based on such coarse-grained measurement the dependence of Bell inequality violation on the degree of coarsening has already been investigated [Phys. Rev. Lett. 112, 010402 (2014)]. In this paper, we first capture the fact that when local-realism is taken to be the defining notion of classicality, the effect of the degree of coarsening on the downfall of quantumness of a macroscopic entangled state can be compensated by testing a Bell-inequality of a higher number of settings from a family of symmetric Bell-inequalities if the number of settings is odd. However, on the contrary, we show that such compensation can not be seen when we witness such quantum-to-classical transition using symmetric Bell inequalities having an even number of settings. Finally, complementing the above result, we show that when unsteerability is taken as the classicality, for both odd and even numbers of settings the degree of coarsening at which the quantum-to-classical transition occurs can be consistently pushed ahead by testing a linear steering inequality of a higher number of settings and observing its violation. We further extend our treatment for mixed macroscopic entangled states

quant-ph↗

arXiv2021

Masking of Quantum Information into Restricted Set of states

Masking of data is a method to protect information by shielding it from a third party, however keeping it usable for further usages like application development, building program extensions to name a few. Whereas it is possible for classical information encoded in composite quantum states to be completely masked from reduced sub-systems, it has to be checked if quantum information can also be masked when the future possibilities of a quantum computer are increasing day by day. Newly proposed no-masking theorem [Phys. Rev. Lett. 120, 230501 (2018)], one of the no-go theorems, demands that except for some restricted sets of non-orthogonal states, it's impossible to mask arbitrary quantum states. Here, we explore the possibility of masking in the IBM quantum experience platform by designing the quantum circuits and running them on the 5-qubit quantum computer. We choose two particular states considering both the orthogonal and non-orthogonal basis states and illustrate their masking through both the theoretical calculation as well as verification in the quantum computer. By quantum state tomography, it is concluded that the experimental results are collected with high fidelity and hence the possibility of masking is realized.

quant-ph↗

arXiv2020

Dynamical Properties and Effects of Quantum Diffraction on The Propagation of E-A-Solitary Waves in Three-Component Fermi Plasma

Electron Acoustic Solitary structures in Fermi Plasma with two temperature electrons have various applications in space and laboratory-made plasma. Formulation of an adequate theory is important to understand various physical systems with various physical parameters. The motion of two temperature electrons in a quantum Fermi plasma system highly affects the solitary profile of the system. We study the quantum Fermi plasma system with two temperature electrons where the streaming velocities of the two-electron population are opposite. We consider the quantum hydrodynamic model (QHD) and derive a linear dispersion relation for the system. For the non-linear study of the system, we use the standard perturbation technique to derive KdV-B equation and show the evolution of a solitary profile with different plasma parameters. We analyze the stable Rouge wave structure using NLSE and show simulation results. We study the dynamical properties and phase plot for a two-stream quantum Fermi plasma system with two temperature electrons.

physics.plasm-ph↗

arXiv2014

An effective AHP-based metaheuristic approach to solve supplier selection problem

The supplier selection problem is based on electing the best supplier from a group of pre-specified candidates, is identified as a Multi Criteria Decision Making (MCDM), is proportionately significant in terms of qualitative and quantitative attributes. It is a fundamental issue to achieve a trade-off between such quantifiable and unquantifiable attributes with an aim to accomplish the best solution to the abovementioned problem. This article portrays a metaheuristic based optimization model to solve this NP-Complete problem. Initially the Analytic Hierarchy Process (AHP) is implemented to generate an initial feasible solution of the problem. Thereafter a Simulated Annealing (SA) algorithm is exploited to improve the quality of the obtained solution. The Taguchi robust design method is exploited to solve the critical issues on the subject of the parameter selection of the SA technique. In order to verify the proposed methodology the numerical results are demonstrated based on tangible industry data.

cs.NE↗

arXiv2012

Neural Network Influence in Group Technology: A Chronological Survey and Critical Analysis

This article portrays a chronological review of the influence of Artificial Neural Network in group technology applications in the vicinity of Cellular Manufacturing Systems. The research trend is identified and the evolvement is captured through a critical analysis of the literature accessible from the very beginning of its practice in the early 90's till the 2010. Analysis of the diverse ANN approaches, spotted research pattern, comparison of the clustering efficiencies, the solutions obtained and the tools used make this study exclusive in its class.

cs.AI↗

arXiv2012

Hybrid Fuzzy-ART based K-Means Clustering Methodology to Cellular Manufacturing Using Operational Time

This paper presents a new hybrid Fuzzy-ART based K-Means Clustering technique to solve the part machine grouping problem in cellular manufacturing systems considering operational time. The performance of the proposed technique is tested with problems from open literature and the results are compared to the existing clustering models such as simple K-means algorithm and modified ART1 algorithm using an efficient modified performance measure known as modified grouping efficiency (MGE) as found in the literature. The results support the better performance of the proposed algorithm. The Novelty of this study lies in the simple and efficient methodology to produce quick solutions for shop floor managers with least computational efforts and time.

cs.LG↗

arXiv2012

An Effective Machine-Part Grouping Algorithm to Construct Manufacturing Cells

The machine-part cell formation problem consists of creating machine cells and their corresponding part families with the objective of minimizing the inter-cell and intra-cell movement while maximizing the machine utilization. This article demonstrates a hybrid clustering approach for the cell formation problem in cellular manufacturing that conjoins Sorenson s similarity coefficient based method to form the production cells. Computational results are shown over the test datasets obtained from the past literature. The hybrid technique is shown to outperform the other methods proposed in literature and including powerful soft computing approaches such as genetic algorithms, genetic programming by exceeding the solution quality on the test problems.

cs.CE↗

arXiv2011

SAPFOCS: a metaheuristic based approach to part family formation problems in group technology

This article deals with Part family formation problem which is believed to be moderately complicated to be solved in polynomial time in the vicinity of Group Technology (GT). In the past literature researchers investigated that the part family formation techniques are principally based on production flow analysis (PFA) which usually considers operational requirements, sequences and time. Part Coding Analysis (PCA) is merely considered in GT which is believed to be the proficient method to identify the part families. PCA classifies parts by allotting them to different families based on their resemblances in: (1) design characteristics such as shape and size, and/or (2) manufacturing characteristics (machining requirements). A novel approach based on simulated annealing namely SAPFOCS is adopted in this study to develop effective part families exploiting the PCA technique. Thereafter Taguchi's orthogonal design method is employed to solve the critical issues on the subject of parameters selection for the proposed metaheuristic algorithm. The adopted technique is therefore tested on 5 different datasets of size 5 {\times} 9 to 27 {\times} 9 and the obtained results are compared with C-Linkage clustering technique. The experimental results reported that the proposed metaheuristic algorithm is extremely effective in terms of the quality of the solution obtained and has outperformed C-Linkage algorithm in most instances.

cs.AI↗