
Rekha SinghalTata Consultancy Services Limited | TCS · TCS Innovation Labs
Rekha Singhal
Phd, Mtech, BE
About
103
Publications
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335
Citations
Introduction
Rekha Singhal currently works as Senior Scientist at the TCS Innovation Labs, Tata Consultancy Services Limited. Rekha does research in Accelerating Software 2.0, High Performance Analytics System, Performance Modelling, analysis and optimization of Big data analytics systems, Databases, Software Engineering and Programming Languages. Their most recent publication is 'SPARK Job Performance Analysis and Prediction Tool'.
Additional affiliations
January 2011 - May 2016
January 2006 - December 2011
August 2011 - April 2016
Publications
Publications (103)
Continuous Latent Space (CLS) and Discrete Latent Space (DLS) models, like AttnUNet and VQUNet, have excelled in medical image segmentation. In contrast, Synergistic Continuous and Discrete Latent Space (CDLS) models show promise in handling fine and coarse-grained information. However, they struggle with modeling long-range dependencies. CLS or CD...
This paper introduces a highly scalable in-memory computing architecture for implementing (1) M-operand, N-bit Boolean functions, viz., AND/NAND/NOR/OR, (2) any arbitrary Boolean function expressed as the sum of products (e.g., F=AB'CD+ABC'D'), (3) N parallel 2-bit XOR operations. Our technique performs operations using a modified 9T SRAM cell-base...
The spatio-temporal complexity of video data presents significant challenges in tasks such as compression, generation, and inpainting. We present four key contributions to address the challenges of spatiotemporal video processing. First, we introduce the 3D Mobile Inverted Vector-Quantization Variational Autoencoder (3D-MBQ-VAE), which combines Var...
In recent times, orthogonal frequency-division multiplexing (OFDM)-based radar has gained wide acceptance given its applicability in joint radar-communication systems. However, realizing such a system on hardware poses a huge area and power bottleneck given its complexity. Therefore it has become ever-important to explore low-power OFDM-based radar...
Machine learning is poised to revolutionize medicine with algorithms that spot cardiac arrhythmia. An automated diagnostic approach can boost the efficacy of diagnosing life-threatening arrhythmia disorders in routine medical procedures. In this paper, we propose a deep learning network CLINet for ECG signal classification. Our network uses convolu...
Approximate computing offers significant gains in efficiency at the cost of minor errors. In this paper, we show that since approximate computing legitimizes controlled impre-cision, this very relaxation can be exploited by an adversary to insert Trojans into approximate circuits. Since the minor errors introduced by the Trojan may be indistinguish...
Early diagnosis plays a pivotal role in effectively treating numerous diseases, especially in healthcare scenarios where prompt and accurate diagnoses are essential. Contrastive learning (CL) has emerged as a promising approach for medical tasks, offering advantages over traditional supervised learning methods. However, in healthcare, patient metad...
Recent advances in pre-trained neural language models have substantially enhanced the performance of numerous natural language processing (NLP) tasks.
However, some existing models require pretraining on a large dataset. Moreover, on using a deep network with sequentially connected transformer blocks, there is a data loss across these blocks. To ov...
Approximate computing (AC) techniques provide overall performance gains in terms of power and energy savings at the cost of minor loss in application accuracy. For this reason, AC has emerged as a viable method for efficiently supporting several compute-intensive applications, e.g., machine learning, deep learning, and image processing, that can to...
Text erasure from an image is helpful for various tasks such as image editing and privacy preservation. We present TPFNet, a novel one-stage network for text removal from images. TPFNet has two parts: feature synthesis and image generation. Since noise can be more effectively removed from low-resolution images, part 1 operates on low-resolution ima...
Approximate computing (AC) techniques provide overall performance gains in terms of power and energy savings at the cost of minor loss in application accuracy. For this reason, AC has emerged as a viable method for efficiently supporting several compute-intensive applications, e.g., machine learning, deep learning, and image processing, that can to...
Text erasure from an image is helpful for various tasks such as image editing and privacy preservation. We present TPFNet, a novel one-stage network for text removal from images. TPFNet has two parts: feature synthesis and image generation. Since noise can be more effectively removed from low-resolution images, part operates on low-resolution image...
Convolutional Neural Network (CNN) models often comprise multiple layers varying in compute requirements. For deployment, a number of hardware accelerators are available that have subtle differences in compute architectures within the same family of platforms. A component (a set of layers) of a CNN model may perceive different performance in differ...
By exploiting the gap between the user's accuracy requirement and the hardware's accuracy capability, approximate circuit design offers enormous gains in efficiency for a minor accuracy loss. In this paper, we propose two approximate floating point multipliers (AxFPMs), named DTCL (decomposition, truncation and chunk-level leading-one quan-tization...
The rapid growth in the volume and complexity of PCB design has encouraged researchers to explore automatic visual inspection of PCB components. Automatic identification of PCB components such as resistors, transistors, etc., can provide several benefits, such as producing a bill of materials, defect detection, and e-waste recycling. Yet, visual id...
Deep neural networks (DNNs) are vulnerable to adversarial inputs, which are created by adding minor perturbations to the genuine inputs. Previous gradient-based adversarial attacks, such as the "fast gradient sign method" (FGSM), add an equal amount (say) of noise to all the pixels of an image. This degrades image quality significantly, such that a...
This paper proposes a novel merged-accumulation-based approximate MAC unit, MEGA-MAC, for accelerating error-resilient applications. MEGA-MAC utilizes a novel rearrangement and compression strategy in the multiplication stage and a novel approximate ``carry predicting adder'' (CPA) in the accumulation stage. Addition and multiplication operations a...
Meta Learning has been in focus in recent years due to the meta-learner model's ability to adapt well and generalize to new tasks, thus, reducing both the time and data requirements for learning. However, a major drawback of meta learner is that, to reach to a state from where learning new tasks becomes feasible with less data, it requires a large...
Achieving maximum possible rate of inferencing with minimum hardware resources plays a major role in reducing enterprise operational costs. In this paper we explore use of PCIe streaming on FPGA based platforms to achieve high throughput. PCIe streaming is a unique capability available on FPGA that eliminates the need for memory copy overheads. We...
We propose the algorithms for performing multiway joins using a new type of coarse grain reconfigurable hardware accelerator – “Plasticine” – that, compared with other accelerators, emphasizes high compute capability and high on-chip communication bandwidth. Joining three or more relations in a single step, i.e. multiway join, is efficient when the...
In this paper, we present iPrescribe, a scalable low-latency architecture for recommending 'next-best-offers' in an online setting. The paper presents the design of iPrescribe and compares its performance for implementations using different real-time streaming technology stacks. iPrescribe uses an ensemble of deep learning and machine learning algo...
We propose the algorithms for performing multiway joins using a new type of coarse grain reconfigurable hardware accelerator~-- ``Plasticine''~-- that, compared with other accelerators, emphasizes high compute capability and high on-chip communication bandwidth. Joining three or more relations in a single step, i.e. multiway join, is efficient when...
Modern real-time business analytic consist of heterogeneous workloads (e.g, database queries, graph processing, and machine learning). These analytic applications need programming environments that can capture all aspects of the constituent workloads (including data models they work on and movement of data across processing engines). Polystore syst...
In this paper we present iPrescribe, a scalable low-latency architecture for recommending 'next-best-offers' in an online setting. The paper presents the design of iPrescribe and compares its performance for implementations using different real-time streaming technology stacks. iPrescribe uses ensemble of deep learning and machine learning algorith...
Distributed big data processing and analytics applications demand a comprehensive end-to-end architecture stack consisting of big data technologies. However, there are many possible architecture patterns (e.g. Lambda, Kappa or Pipeline architectures) to choose from when implementing the application requirements. A big data technology in isolation m...
Spark is one of most widely deployed in-memory big data technology for parallel data processing across cluster of machines. The availability of these big data platforms on commodity machines has raised the challenge of assuring performance of applications with increase in data size. We have build a tool to assist application developer and tester to...
The wide availability of open source big data processing
frameworks, such as Spark, has increased migration of existing appli-
cations and deployment of new applications to these cost-e�ective plat-
forms. One of the challenges is assuring performance of an application
with increase in data size in production system. We have addressed this
problem...
Typically, applications are tested on small data size for both functional and non functional requirements. However, in production environment, the applications, having SQL queries, may experience performance violations due to increase in data volume. There is need to have tool which could test SQL query performance for large data sizes without elon...
Application and/or data migration is a result of limitations in existing system architecture to handle new requirements and the availability of newer, more efficient technology. In any big data architecture, technology migration is staggered across multiple levels and poses functional (related to components of the architecture and underlying infras...
Nowadays applications are migrating from traditional 3-tier architecture to Big data platform which are widely available in open source and can do parallel data processing on cluster of commodity machines. The challenges are to choose the “right” available Big data framework for an application with the available features of the framework. We have p...
In a production system, increase in data size will increase the execution time of the application's SQL queries and degrade its performance. Tuning SQL queries in production requires additional efforts and cost. Time constraints during application development do not permit testing SQL queries with high data volumes. Having the capability to predict...
Digitization of user services and cheap access to the internet has led to two critical problems- quick response to end-user queries and faster analysis of large accumulated data to serve users better. This has also led to the advent of various big data processing technologies, each of them has architecture specific parameters to tune for optimal ex...
Performance model solvers and simulation engines have been around for more than two decades. Yet, performance modeling has not received wide acceptance in the software industry, unlike pervasion of modeling and simulation tools in other industries. This paper explores underlying causes and looks at challenges that eed to be overcome to increase uti...
The paradigm of big data demands either extension of existing benchmarks or building new benchmarks to capture the diversity of data and impact of change in data size and/or system size. This has led to increase in cycle time of benchmarking an application which includes multiple workloads executions on different data sizes. This paper addresses th...
The first ACM international workshop on performance analysis of big data system is held in Austin, Texas, USA on February 1, 2015 and co-located with the ACM fifth International Conference on Performance Engineering (ICPE). The main objective of the workshop is to discuss the performance challenges imposed by big data systems and the different stat...
In a typical database application environment, database queries have a major share in contributing to application's response time. A database query elapsed response time (ERT) primarily consists of time spent on the input/output (IO) access including storage subsystem and network transfer, and CPU processing, which changes with change in size of th...
In a typical database application development, requirement is to optimize SQL queries to meet service level agreements (SLA); the optimized queries are tested on the application development database which is some fraction of the production database. As time progresses the database grows and the earlier optimized queries may not hold SLA anymore. On...
Health is of major concern among people. There are various factors which could affect human health. In this paper we propose a framework to analyze various environmental parameters like air and water pollutants and to understand their impact on human health. The study is confined to understand the diseases which may be caused by these parameters. T...
In a typical OLTP environment, emphasis has been given on promising Service level Agreements (SLAs) for perceived query elapsed response time; the SQL queries are tested on the small size of database which may be a fraction of the production database. As time progresses the database grows and the earlier optimized queries may not hold SLA anymore....
Information Technology (IT) has touched and changed the lives of the younger generation in India in many ways but IT still has its role to play in making a significant impact to the quality of lives of the senior citizens of our country. This study is undertaken to address the objective of using technology to provide affordable health care and othe...
This paper presents a framework for public healthcare by making a grid over public infrastructure such as Internet. It clearly illustrates the need and viability of such grids. The paper gives in details the technology required behind building such global health grid and the issues to overcome for building public health care grid.
This paper focuses on implementation of cascaded configuration of initiator-target pair in NetBSD environment. The use of existing independent initiator and target for cascading has some drawbacks, which can be handled by combining pair of target and initiator as proposed in this paper. We also show the performance comparisons between proposed and...
This paper presents a solution for optimal business continuity, with storage architecture for enterprise applications, which shall ensure negligible data loss and quick recovery. The solution makes use of IP SAN, which are used for data management without burdening the application server, as well as replication techniques to replicate data to remot...
In this paper we propose a design and implementation for efficient semi-synchronous replication solution using iSCSI for disaster recovery. We replicate the data at block level to bring in efficiency. Further we use features of database application which helps in reducing the complexity and improving the performance of the disaster recovery solutio...