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Sudarshan Srinivasan
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2020 – today
- 2024
- [j4]Chandra Sekhar Mummidi
, Victor da Cruz Ferreira
Highly Efficient Self-checking Matrix Multiplication on Tiled AMX Accelerators. ACM Trans. Archit. Code Optim. 21(2): 21 (2024) - [c35]Camila Roa
Semantic Stealth: Crafting Covert Adversarial Patches for Sentiment Classifiers Using Large Language Models. AISec@CCS 2024: 42-52 - [c34]William Won, Saeed Rashidi, Sudarshan Srinivasan, Tushar Krishna:
LIBRA: Enabling Workload-Aware Multi-Dimensional Network Topology Optimization for Distributed Training of Large AI Models. ISPASS 2024: 205-216 - [c33]William Won, Midhilesh Elavazhagan, Sudarshan Srinivasan, Swati Gupta, Tushar Krishna:
TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Machine Learning. MICRO 2024: 856-870 - [i19]Ran Elgedawy, Sudarshan Srinivasan, Ioana Danciu:
Dynamic Q&A of Clinical Documents with Large Language Models. CoRR abs/2401.10733 (2024) - [i18]Maria Mahbub
Leveraging Large Language Models to Extract Information on Substance Use Disorder Severity from Clinical Notes: A Zero-shot Learning Approach. CoRR abs/2403.12297 (2024) - [i17]Abhimanyu Bambhaniya, Ritik Raj, Geonhwa Jeong, Souvik Kundu, Sudarshan Srinivasan, Midhilesh Elavazhagan, Madhu Kumar, Tushar Krishna:
Demystifying Platform Requirements for Diverse LLM Inference Use Cases. CoRR abs/2406.01698 (2024) - [i16]Saeed Rashidi, William Won, Sudarshan Srinivasan, Puneet Gupta, Tushar Krishna:
FRED: Flexible REduction-Distribution Interconnect and Communication Implementation for Wafer-Scale Distributed Training of DNN Models. CoRR abs/2406.19580 (2024) - [i15]Sudarshan Srinivasan, Maria Mahbub, Amir Sadovnik:
Advancing NLP Security by Leveraging LLMs as Adversarial Engines. CoRR abs/2410.18215 (2024) - 2023
- [c32]Sandeep Bal, Chandra Sekhar Mummidi, Victor da Cruz Ferreira, Sudarshan Srinivasan, Sandip Kundu:
A Novel Fault-Tolerant Architecture for Tiled Matrix Multiplication. DATE 2023: 1-6 - [c31]William Won
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale. ISPASS 2023: 283-294 - [i14]William Won, Taekyung Heo, Saeed Rashidi, Srinivas Sridharan, Sudarshan Srinivasan, Tushar Krishna:
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale. CoRR abs/2303.14006 (2023) - [i13]William Won, Midhilesh Elavazhagan, Sudarshan Srinivasan, Ajaya Durg, Swati Gupta, Tushar Krishna:
TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Training. CoRR abs/2304.05301 (2023) - [i12]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:
Question-Answering System Extracts Information on Injection Drug Use from Clinical Progress Notes. CoRR abs/2305.08777 (2023) - 2022
- [j3]Maria Mahbub
BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task. Bioinform. 38(18): 4369-4379 (2022) - [c30]Chandra Sekhar Mummidi, Sandeep Bal, Brunno F. Goldstein, Sudarshan Srinivasan, Sandip Kundu:
A Highly-Efficient Error Detection Technique for General Matrix Multiplication using Tiled Processing on SIMD Architecture. ICCD 2022: 529-536 - [c29]Saeed Rashidi, William Won
Themis: a network bandwidth-aware collective scheduling policy for distributed training of DL models. ISCA 2022: 581-596 - [c28]Edmon Begoli, Sudarshan Srinivasan, Maria Mahbub
Improving Efficiency and Robustness of Transformer-based Information Retrieval Systems. SIGIR 2022: 3433-3435 - [i11]Maria Mahbub, Sudarshan Srinivasan, Edmon Begoli, Gregory D. Peterson:
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task. CoRR abs/2202.13174 (2022) - 2021
- [c27]Edmon Begoli, Seung-Hwan Lim, Sudarshan Srinivasan:
Performance Profile of Transformer Fine-Tuning in Multi-GPU Cloud Environments. IEEE BigData 2021: 3095-3100 - [c26]Eric Qin, Geonhwa Jeong, William Won
Extending Sparse Tensor Accelerators to Support Multiple Compression Formats. IPDPS 2021: 1014-1024 - [c25]Saeed Rashidi, Matthew Denton, Srinivas Sridharan, Sudarshan Srinivasan, Amoghavarsha Suresh, Jade Nie, Tushar Krishna:
Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms. ISCA 2021: 540-553 - [c24]Brunno F. Goldstein, Victor da Cruz Ferreira, Sudarshan Srinivasan, Dipankar Das, Alexandre Solon Nery, Sandip Kundu, Felipe M. G. França
A Lightweight Error-Resiliency Mechanism for Deep Neural Networks. ISQED 2021: 311-316 - [c23]Sudarshan Srinivasan, Edmon Begoli, Maria Mahbub
Nomen est Omen - The Role of Signatures in Ascribing Email Author Identity with Transformer Neural Networks. SP (Workshops) 2021: 291-297 - [i10]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, Quan Zhou, Edmon Begoli:
The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations. CoRR abs/2102.11917 (2021) - [i9]Eric Qin, Geonhwa Jeong, William Won, Sheng-Chun Kao, Hyoukjun Kwon, Sudarshan Srinivasan, Dipankar Das, Gordon Euhyun Moon, Sivasankaran Rajamanickam, Tushar Krishna:
Extending Sparse Tensor Accelerators to Support Multiple Compression Formats. CoRR abs/2103.10452 (2021) - [i8]William Won, Saeed Rashidi, Sudarshan Srinivasan, Tushar Krishna:
Exploring Multi-dimensional Hierarchical Network Topologies for Efficient Distributed Training of Trillion Parameter DL Models. CoRR abs/2109.11762 (2021) - [i7]Saeed Rashidi, William Won, Sudarshan Srinivasan, Srinivas Sridharan, Tushar Krishna:
Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models. CoRR abs/2110.04478 (2021) - 2020
- [c22]Edmon Begoli, Sudarshan Srinivasan, Maria Mahbub:
The Transformers for Polystores - The Next Frontier for Polystore Research. Poly/DMAH@VLDB 2020: 72-77 - [c21]Eric Qin, Ananda Samajdar, Hyoukjun Kwon
SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training. HPCA 2020: 58-70 - [c20]Saeed Rashidi, Srinivas Sridharan, Sudarshan Srinivasan, Tushar Krishna:
ASTRA-SIM: Enabling SW/HW Co-Design Exploration for Distributed DL Training Platforms. ISPASS 2020: 81-92 - [c19]Brunno F. Goldstein
Reliability Evaluation of Compressed Deep Learning Models. LASCAS 2020: 1-5 - [c18]Dhiraj D. Kalamkar, Evangelos Georganas, Sudarshan Srinivasan, Jianping Chen, Mikhail Shiryaev, Alexander Heinecke:
Optimizing deep learning recommender systems training on CPU cluster architectures. SC 2020: 43 - [i6]Dhiraj D. Kalamkar, Evangelos Georganas, Sudarshan Srinivasan, Jianping Chen, Mikhail Shiryaev, Alexander Heinecke:
Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures. CoRR abs/2005.04680 (2020) - [i5]Saeed Rashidi, Srinivas Sridharan, Sudarshan Srinivasan, Matthew Denton, Tushar Krishna:
Efficient Communication Acceleration for Next-Gen Scale-up Deep Learning Training Platforms. CoRR abs/2007.00156 (2020)
2010 – 2019
- 2019
- [c17]Dhiraj D. Kalamkar, Kunal Banerjee, Sudarshan Srinivasan, Srinivas Sridharan, Evangelos Georganas, Mikhail E. Smorkalov
Training Google Neural Machine Translation on an Intel CPU Cluster. CLUSTER 2019: 1-10 - [i4]Dhiraj D. Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das, Kunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, Nataraj Jammalamadaka, Jianyu Huang, Hector Yuen, Jiyan Yang, Jongsoo Park, Alexander Heinecke, Evangelos Georganas, Sudarshan Srinivasan, Abhisek Kundu, Misha Smelyanskiy, Bharat Kaul, Pradeep Dubey:
A Study of BFLOAT16 for Deep Learning Training. CoRR abs/1905.12322 (2019) - [i3]Naveen Mellempudi, Sudarshan Srinivasan, Dipankar Das, Bharat Kaul:
Mixed Precision Training With 8-bit Floating Point. CoRR abs/1905.12334 (2019) - [i2]Sudarshan Srinivasan, Pradeep Janedula, Saurabh Dhoble, Sasikanth Avancha, Dipankar Das, Naveen Mellempudi, Bharat Daga, Martin Langhammer, Gregg Baeckler, Bharat Kaul:
High Performance Scalable FPGA Accelerator for Deep Neural Networks. CoRR abs/1908.11809 (2019) - [i1]Abhisek Kundu, Sudarshan Srinivasan, Eric C. Qin, Dhiraj D. Kalamkar, Naveen K. Mellempudi, Dipankar Das, Kunal Banerjee, Bharat Kaul, Pradeep Dubey:
K-TanH: Hardware Efficient Activations For Deep Learning. CoRR abs/1909.07729 (2019) - 2016
- [j2]Sudarshan Srinivasan, Nithesh kurella, Israel Koren, Sandip Kundu
Exploring Heterogeneity within a Core for Improved Power Efficiency. IEEE Trans. Parallel Distributed Syst. 27(4): 1057-1069 (2016) - [c16]Sudarshan Srinivasan, Israel Koren, Sandip Kundu
Improving performance per Watt of non-monotonic Multicore Processors via bottleneck-based online program phase classification. ICCD 2016: 528-535 - [c15]Sudarshan Srinivasan, Nithesh kurella, Israel Koren, Sandip Kundu:
Dynamic Reconfiguration vs. DVFS: A Comparative Study on Power Efficiency of Processors. VLSID 2016: 563-564 - 2015
- [c14]Sudarshan Srinivasan, Israel Koren, Sandip Kundu:
Online mechanism for reliability and power-efficiency management of a dynamically reconfigurable core. ICCD 2015: 327-334 - [c13]Hamed Sajjadi Kia, Cristinel Ababei, Sudarshan Srinivasan, Shaista Jabeen:
A new scalable fault tolerant routing algorithm for networks-on-chip. MWSCAS 2015: 1-4 - 2014
- [c12]Sudarshan Srinivasan, Nithesh kurella, Israel Koren, Rance Rodrigues, Sandip Kundu:
A runtime support mechanism for fast mode switching of a self-morphing core for power efficiency. PACT 2014: 491-492 - [c11]Daniel Aceituna, Hyunsook Do, Sudarshan Srinivasan:
A systematic approach to transforming system requirements into model checking specifications. ICSE Companion 2014: 165-174 - [c10]Sudarshan Srinivasan, Gideon Juve, Rafael Ferreira da Silva
A cleanup algorithm for implementing storage constraints in scientific workflow executions. WORKS@SC 2014: 41-49 - [c9]Sudarshan Srinivasan, Victor Hazlewood, Gregory D. Peterson:
Descriptive Data Analysis of File Transfer Data. XSEDE 2014: 37:1-37:8 - 2013
- [c8]Sudarshan Srinivasan, Rance Rodrigues, Arunachalam Annamalai, Israel Koren, Sandip Kundu:
On dynamic polymorphing of a superscalar core for improving energy efficiency. ICCD 2013: 495-498 - [c7]Sudarshan Srinivasan, Rance Rodrigues, Arunachalam Annamalai, Israel Koren, Sandip Kundu:
A study on polymorphing superscalar processor dynamically to improve power efficiency. ISVLSI 2013: 46-51 - [c6]Sudarshan Srinivasan, Raghavan Kumar, Sandip Kundu:
Program phase duration prediction and its application to fine-grain power management. ISVLSI 2013: 127-132 - [c5]Vinay C. Patil, Sudarshan Srinivasan, Wayne P. Burleson, Sandip Kundu:
Impact of Clock-Gating on Power Distribution Network Using Wavelet Analysis. VLSI Design 2013: 80-85 - 2012
- [j1]Sudarshan Srinivasan, Kunal P. Ganeshpure, Sandip Kundu:
A Wavelet-Based Spatio-Temporal Heat Dissipation Model for Reordering of Program Phases to Produce Temperature Extremes in a Chip. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 31(12): 1867-1880 (2012) - [c4]Victor Tomashevich, Sudarshan Srinivasan, Fabian Foerg, Ilia Polian:
Cross-level protection of circuits against faults and malicious attacks. IOLTS 2012: 150-155 - [c3]Sudarshan Srinivasan, Sandip Kundu:
Functional test pattern generation for maximizing temperature in 3D IC chip stack. ISQED 2012: 109-116 - 2011
- [c2]Sudarshan Srinivasan, Bharath Phanibhushana, Arunkumar Vijayakumar, Sandip Kundu:
Stress aware switching activity driven low power design of critical paths in nanoscale CMOS circuits. ACM Great Lakes Symposium on VLSI 2011: 265-270 - [c1]Sudarshan Srinivasan, Kunal P. Ganeshpure, Sandip Kundu:
Maximizing hotspot temperature: Wavelet based modelling of heating and cooling profile of functional workloads. ISQED 2011: 559-565
Coauthor Index
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last updated on 2024-12-11 21:42 CET by the dblp team
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