Stress aware switching activity driven low power design of critical paths in nanoscale CMOS circuits. - Sudarshan Srinivasan; Bharath Phanibhushana; Arunkumar Vijayakumar; Sandip Kundu (ACM Great Lakes Symposium on VLSI, 2011)

Maximizing hotspot temperature: Wavelet based modelling of heating and cooling profile of functional workloads. - Sudarshan Srinivasan; Kunal P. Ganeshpure; Sandip Kundu (ISQED, 2011)

Cross-level protection of circuits against faults and malicious attacks. - Victor Tomashevich; Sudarshan Srinivasan; Fabian Foerg; Ilia Polian (IOLTS, 2012)

Functional test pattern generation for maximizing temperature in 3D IC chip stack. - Sudarshan Srinivasan; Sandip Kundu (ISQED, 2012)

A Wavelet-Based Spatio-Temporal Heat Dissipation Model for Reordering of Program Phases to Produce Temperature Extremes in a Chip. - Sudarshan Srinivasan; Kunal P. Ganeshpure; Sandip Kundu (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2012)

On dynamic polymorphing of a superscalar core for improving energy efficiency. - Sudarshan Srinivasan; Rance Rodrigues; Arunachalam Annamalai; Israel Koren; Sandip Kundu (ICCD, 2013)

Program phase duration prediction and its application to fine-grain power management. - Sudarshan Srinivasan; Raghavan Kumar; Sandip Kundu (ISVLSI, 2013)

A study on polymorphing superscalar processor dynamically to improve power efficiency. - Sudarshan Srinivasan; Rance Rodrigues; Arunachalam Annamalai; Israel Koren; Sandip Kundu (ISVLSI, 2013)

Impact of Clock-Gating on Power Distribution Network Using Wavelet Analysis. - Vinay C. Patil; Sudarshan Srinivasan; Wayne P. Burleson; Sandip Kundu (VLSI Design, 2013)

A runtime support mechanism for fast mode switching of a self-morphing core for power efficiency. - Sudarshan Srinivasan; Nithesh kurella; Israel Koren; Rance Rodrigues; Sandip Kundu (PACT, 2014)

A systematic approach to transforming system requirements into model checking specifications. - Daniel Aceituna; Hyunsook Do; Sudarshan Srinivasan (ICSE Companion, 2014)

A cleanup algorithm for implementing storage constraints in scientific workflow executions. - Sudarshan Srinivasan; Gideon Juve; Rafael Ferreira da Silva; Karan Vahi; Ewa Deelman (WORKS@SC, 2014)

Descriptive Data Analysis of File Transfer Data. - Sudarshan Srinivasan; Victor Hazlewood; Gregory D. Peterson (XSEDE, 2014)

Online mechanism for reliability and power-efficiency management of a dynamically reconfigurable core. - Sudarshan Srinivasan; Israel Koren; Sandip Kundu (ICCD, 2015)

A new scalable fault tolerant routing algorithm for networks-on-chip. - Hamed Sajjadi Kia; Cristinel Ababei; Sudarshan Srinivasan; Shaista Jabeen (MWSCAS, 2015)

Improving performance per Watt of non-monotonic Multicore Processors via bottleneck-based online program phase classification. - Sudarshan Srinivasan; Israel Koren; Sandip Kundu (ICCD, 2016)

Dynamic Reconfiguration vs. DVFS: A Comparative Study on Power Efficiency of Processors. - Sudarshan Srinivasan; Nithesh kurella; Israel Koren; Sandip Kundu (VLSID, 2016)

Exploring Heterogeneity within a Core for Improved Power Efficiency. - Sudarshan Srinivasan; Nithesh kurella; Israel Koren; Sandip Kundu (IEEE Trans. Parallel Distributed Syst., 2016)

SynthNotes: A Generator Framework for High-volume, High-fidelity Synthetic Mental Health Notes. - Edmon Begoli; Kris Brown; Sudarshan Srinivasan; Suzanne Tamang (IEEE BigData, 2018)

Evaluating Text Analytic Frameworks for Mental Health Surveillance. - Benjamin Mayer; Joshua Arnold; Edmon Begoli; Everett Neil Rush; Michael Drewry; Kris Brown; Eduardo Ponce; Sudarshan Srinivasan (ICDE Workshops, 2018)

Training Google Neural Machine Translation on an Intel CPU Cluster. - Dhiraj D. Kalamkar; Kunal Banerjee 001; Sudarshan Srinivasan; Srinivas Sridharan 002; Evangelos Georganas; Mikhail E. Smorkalov; Cong Xu; Alexander Heinecke (CLUSTER, 2019)

A Study of BFLOAT16 for Deep Learning Training. - Dhiraj D. Kalamkar; Dheevatsa Mudigere; Naveen Mellempudi; Dipankar Das 002; Kunal Banerjee 001; Sasikanth Avancha; Dharma Teja Vooturi; Nataraj Jammalamadaka; Jianyu Huang; Hector Yuen; Jiyan Yang; Jongsoo Park; et al. (CoRR, 2019)

Mixed Precision Training With 8-bit Floating Point. - Naveen Mellempudi; Sudarshan Srinivasan; Dipankar Das 002; Bharat Kaul (CoRR, 2019)

High Performance Scalable FPGA Accelerator for Deep Neural Networks. - Sudarshan Srinivasan; Pradeep Janedula; Saurabh Dhoble; Sasikanth Avancha; Dipankar Das 002; Naveen Mellempudi; Bharat Daga; Martin Langhammer; Gregg Baeckler; Bharat Kaul (CoRR, 2019)

K-TanH: Hardware Efficient Activations For Deep Learning. - Abhisek Kundu; Sudarshan Srinivasan; Eric C. Qin; Dhiraj D. Kalamkar; Naveen K. Mellempudi; Dipankar Das 002; Kunal Banerjee 001; Bharat Kaul; Pradeep Dubey (CoRR, 2019)

The Transformers for Polystores - The Next Frontier for Polystore Research. - Edmon Begoli; Sudarshan Srinivasan; Maria Mahbub (Poly/DMAH@VLDB, 2020)

SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training. - Eric Qin 001; Ananda Samajdar; Hyoukjun Kwon; Vineet Nadella; Sudarshan Srinivasan; Dipankar Das 002; Bharat Kaul; Tushar Krishna (HPCA, 2020)

ASTRA-SIM: Enabling SW/HW Co-Design Exploration for Distributed DL Training Platforms. - Saeed Rashidi; Srinivas Sridharan 002; Sudarshan Srinivasan; Tushar Krishna (ISPASS, 2020)

Reliability Evaluation of Compressed Deep Learning Models. - Brunno F. Goldstein; Sudarshan Srinivasan; Dipankar Das 002; Kunal Banerjee 001; Leandro Santiago de Araújo; Victor da Cruz Ferreira; Alexandre Solon Nery; Sandip Kundu; Felipe M. G. França (LASCAS, 2020)

Optimizing deep learning recommender systems training on CPU cluster architectures. - Dhiraj D. Kalamkar; Evangelos Georganas; Sudarshan Srinivasan; Jianping Chen; Mikhail Shiryaev; Alexander Heinecke (SC, 2020)

Efficient Communication Acceleration for Next-Gen Scale-up Deep Learning Training Platforms. - Saeed Rashidi; Srinivas Sridharan 002; Sudarshan Srinivasan; Matthew Denton; Tushar Krishna (CoRR, 2020)

Performance Profile of Transformer Fine-Tuning in Multi-GPU Cloud Environments. - Edmon Begoli; Seung-Hwan Lim; Sudarshan Srinivasan (IEEE BigData, 2021)

Extending Sparse Tensor Accelerators to Support Multiple Compression Formats. - Eric Qin 001; Geonhwa Jeong; William Won; Sheng-Chun Kao; Hyoukjun Kwon; Sudarshan Srinivasan; Dipankar Das 002; Gordon Euhyun Moon; Sivasankaran Rajamanickam; Tushar Krishna (IPDPS, 2021)

Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms. - Saeed Rashidi; Matthew Denton; Srinivas Sridharan 002; Sudarshan Srinivasan; Amoghavarsha Suresh; Jade Nie; Tushar Krishna (ISCA, 2021)

A Lightweight Error-Resiliency Mechanism for Deep Neural Networks. - Brunno F. Goldstein; Victor da Cruz Ferreira; Sudarshan Srinivasan; Dipankar Das 002; Alexandre Solon Nery; Sandip Kundu; Felipe M. G. França (ISQED, 2021)

Nomen est Omen - The Role of Signatures in Ascribing Email Author Identity with Transformer Neural Networks. - Sudarshan Srinivasan; Edmon Begoli; Maria Mahbub; Kathryn Knight (SP (Workshops), 2021)

The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations. - Jeremiah Duncan; Fabian Fallas; Chris Gropp; Emily Herron; Maria Mahbub; Paula Olaya; Eduardo Ponce; Tabitha K. Samuel; Daniel Schultz; Sudarshan Srinivasan; Maofeng Tang; Viktor Zenkov; et al. (CoRR, 2021)

Exploring Multi-dimensional Hierarchical Network Topologies for Efficient Distributed Training of Trillion Parameter DL Models. - William Won; Saeed Rashidi; Sudarshan Srinivasan; Tushar Krishna (CoRR, 2021)

A Highly-Efficient Error Detection Technique for General Matrix Multiplication using Tiled Processing on SIMD Architecture. - Chandra Sekhar Mummidi; Sandeep Bal; Brunno F. Goldstein; Sudarshan Srinivasan; Sandip Kundu (ICCD, 2022)

Themis: a network bandwidth-aware collective scheduling policy for distributed training of DL models. - Saeed Rashidi; William Won; Sudarshan Srinivasan; Srinivas Sridharan 002; Tushar Krishna (ISCA, 2022)

Improving Efficiency and Robustness of Transformer-based Information Retrieval Systems. - Edmon Begoli; Sudarshan Srinivasan; Maria Mahbub (SIGIR, 2022)

BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task. - Maria Mahbub; Sudarshan Srinivasan; Edmon Begoli; Gregory D. Peterson (Bioinform., 2022)

A Novel Fault-Tolerant Architecture for Tiled Matrix Multiplication. - Sandeep Bal; Chandra Sekhar Mummidi; Victor da Cruz Ferreira; Sudarshan Srinivasan; Sandip Kundu (DATE, 2023)

ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale. - William Won; Taekyung Heo; Saeed Rashidi; Srinivas Sridharan 002; Sudarshan Srinivasan; Tushar Krishna (ISPASS, 2023)

TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Training. - William Won; Midhilesh Elavazhagan; Sudarshan Srinivasan; Ajaya Durg; Swati Gupta 001; Tushar Krishna (CoRR, 2023)

Question-Answering System Extracts Information on Injection Drug Use from Clinical Progress Notes. - Maria Mahbub; Ian Goethert; Ioana Danciu; Kathryn Knight; Sudarshan Srinivasan; Suzanne Tamang; Karine Rozenberg-Ben-Dror; Hugo Solares; Susana B. Martins; Edmon Begoli; Gregory D. Peterson (CoRR, 2023)

Semantic Stealth: Crafting Covert Adversarial Patches for Sentiment Classifiers Using Large Language Models. - Camila Roa; Maria Mahbub; Sudarshan Srinivasan; Edmon Begoli; Amir Sadovnik (AISec@CCS, 2024)

LIBRA: Enabling Workload-Aware Multi-Dimensional Network Topology Optimization for Distributed Training of Large AI Models. - William Won; Saeed Rashidi; Sudarshan Srinivasan; Tushar Krishna (ISPASS, 2024)

TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Machine Learning. - William Won; Midhilesh Elavazhagan; Sudarshan Srinivasan; Swati Gupta 001; Tushar Krishna (MICRO, 2024)

Dynamic Q&A of Clinical Documents with Large Language Models. - Ran Elgedawy; Sudarshan Srinivasan; Ioana Danciu (CoRR, 2024)

Leveraging Large Language Models to Extract Information on Substance Use Disorder Severity from Clinical Notes: A Zero-shot Learning Approach. - Maria Mahbub; Gregory M. Dams; Sudarshan Srinivasan; Caitlin Rizy; Ioana Danciu; Jodie Trafton; Kathryn Knight (CoRR, 2024)

Demystifying Platform Requirements for Diverse LLM Inference Use Cases. - Abhimanyu Bambhaniya; Ritik Raj; Geonhwa Jeong; Souvik Kundu 009; Sudarshan Srinivasan; Midhilesh Elavazhagan; Madhu Kumar; Tushar Krishna (CoRR, 2024)

FRED: Flexible REduction-Distribution Interconnect and Communication Implementation for Wafer-Scale Distributed Training of DNN Models. - Saeed Rashidi; William Won; Sudarshan Srinivasan; Puneet Gupta 001; Tushar Krishna (CoRR, 2024)

Advancing NLP Security by Leveraging LLMs as Adversarial Engines. - Sudarshan Srinivasan; Maria Mahbub; Amir Sadovnik (CoRR, 2024)

Highly Efficient Self-checking Matrix Multiplication on Tiled AMX Accelerators. - Chandra Sekhar Mummidi; Victor da Cruz Ferreira; Sudarshan Srinivasan; Sandip Kundu (ACM Trans. Archit. Code Optim., 2024)

A Compound Data Poisoning Technique with Significant Adversarial Effects on Transformer-based Sentiment Classification Tasks. - Edmon Begoli; Maria Mahbub; Linsey Passarella; Sudarshan Srinivasan (ACM J. Data Inf. Qual., 2024)

FRED: A Wafer-scale Fabric for 3D Parallel DNN Training. - Saeed Rashidi; William Won; Sudarshan Srinivasan; Puneet Gupta 001; Tushar Krishna (ISCA, 2025)

Understanding and Optimizing Multi-Stage AI Inference Pipelines. - Abhimanyu Rajeshkumar Bambhaniya; Hanjiang Wu; Suvinay Subramanian; Sudarshan Srinivasan; Souvik Kundu 009; Amir Yazdanbakhsh; Midhilesh Elavazhagan; Madhu Kumar; Tushar Krishna (CoRR, 2025)