Shubham Jain

According to our database, Shubham Jain authored at least 31 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

CiMBA: Accelerating Genome Sequencing Through On-Device Basecalling via Compute-in-Memory.

William Andrew Simon, Irem Boybat, Riselda Kodra, Elena Ferro, Gagandeep Singh, Mohammed Alser, Shubham Jain, Hsinyu Tsai, Geoffrey W. Burr, Onur Mutlu, Abu Sebastian

IEEE Trans. Parallel Distributed Syst., June, 2025

Power-Limited Inference Performance Optimization Using a Software-Assisted Peak Current Regulation Scheme in a 5-nm AI SoC.

Monodeep Kar, Joel Silberman, Swagath Venkataramani, Viji Srinivasan, Bruce M. Fleischer, Joshua Rubin, JohnDavid Lancaster, Sae Kyu Lee, Matthew Cohen, Matthew M. Ziegler, Nianzheng Cao, Sandra Woodward, Ankur Agrawal, Ching Zhou, Prasanth Chatarasi, Thomas Gooding, Michael Guillorn, Bahman Hekmatshoartabari, Philip Jacob, Radhika Jain, Shubham Jain, Jinwook Jung, Kyu-Hyoun Kim, Siyu Koswatta, Martin Lutz, Alberto Mannari, Abey Mathew, Indira Nair, Ashish Ranjan, Zhibin Ren, Scot Rider, Thomas Röwer, David L. Satterfield, Marcel Schaal, Sanchari Sen, Gustavo Tellez, Hung Tran, Wei Wang, Vidhi Zalani, Jintao Zhang, Xin Zhang, Vinay Shah, Robert M. Senger, Arvind Kumar, Pong-Fei Lu, Leland Chang

IEEE J. Solid State Circuits, January, 2025

2024

DNNDaSher: A Compiler Framework for Dataflow Compatible End-to-End Acceleration on IBM AIU.

Sanchari Sen, Shubham Jain, Sarada Krithivasan, Swagath Venkataramani, Vijayalakshmi Srinivasan

IEEE Micro, 2024

14.1 A Software-Assisted Peak Current Regulation Scheme to Improve Power-Limited Inference Performance in a 5nm AI SoC.

Monodeep Kar, Joel Silberman, Swagath Venkataramani, Viji Srinivasan, Bruce M. Fleischer, Joshua Rubin, JohnDavid Lancaster, Sae Kyu Lee, Matthew Cohen, Matthew M. Ziegler, Nianzheng Cao, Sandra Woodward, Ankur Agrawal, Ching Zhou, Prasanth Chatarasi, Thomas Gooding, Michael Guillorn, Bahman Hekmatshoartabari, Philip Jacob, Radhika Jain, Shubham Jain, Jinwook Jung, Kyu-Hyoun Kim, Siyu Koswatta, Martin Lutz, Alberto Mannari, Abey Mathew, Indira Nair, Ashish Ranjan, Zhibin Ren, Scot Rider, Thomas Roewer, David L. Satterfield, Marcel Schaal, Sanchari Sen, Gustavo Tellez, Hung Tran, Wei Wang, Vidhi Zalani, Jintao Zhang, Xin Zhang, Vinay Shah, Robert M. Senger, Arvind Kumar, Pong-Fei Lu, Leland Chang

Proceedings of the IEEE International Solid-State Circuits Conference, 2024

2023

A Heterogeneous and Programmable Compute-In-Memory Accelerator Architecture for Analog-AI Using Dense 2-D Mesh.

Shubham Jain, Hsinyu Tsai, Ching-Tzu Chen, Ramachandran Muralidhar, Irem Boybat, Martin M. Frank, Stanislaw Wozniak, Milos Stanisavljevic, Praneet Adusumilli, Pritish Narayanan, Kohji Hosokawa, Masatoshi Ishii, Arvind Kumar, Vijay Narayanan, Geoffrey W. Burr

IEEE Trans. Very Large Scale Integr. Syst., 2023

Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited).

Hsinyu Tsai, Pritish Narayanan, Shubham Jain, Stefano Ambrogio, Kohji Hosokawa, Masatoshi Ishii, Charles Mackin, Ching-Tzu Chen, Atsuya Okazaki, Akiyo Nomura, Irem Boybat, Ramachandran Muralidhar, Martin M. Frank, Takeo Yasuda, Alexander M. Friz, Yasuteru Kohda, An Chen, Andrea Fasoli, Malte J. Rasch, Stanislaw Wozniak, Jose Luquin, Vijay Narayanan, Geoffrey W. Burr

Proceedings of the IEEE International Symposium on Circuits and Systems, 2023

Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited).

Martin M. Frank, Ning Li, Malte J. Rasch, Shubham Jain, Ching-Tzu Chen, Ramachandran Muralidhar, Jin-Ping Han, Vijay Narayanan, Timothy Philip, Kevin Brew, Andrew Simon, Iqbal Saraf, Nicole Saulnier, Irem Boybat, Stanislaw Wozniak, Abu Sebastian, Pritish Narayanan, Charles Mackin, An Chen, Hsinyu Tsai, Geoffrey W. Burr

Proceedings of the IEEE International Reliability Physics Symposium, 2023

2022

Ax-BxP: Approximate Blocked Computation for Precision-reconfigurable Deep Neural Network Acceleration.

Reena Elangovan, Shubham Jain, Anand Raghunathan

ACM Trans. Design Autom. Electr. Syst., 2022

2021

TxSim: Modeling Training of Deep Neural Networks on Resistive Crossbar Systems.

Sourjya Roy, Shrihari Sridharan, Shubham Jain, Anand Raghunathan

IEEE Trans. Very Large Scale Integr. Syst., 2021

RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars.

Shubham Jain, Abhronil Sengupta, Kaushik Roy, Anand Raghunathan

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2021

RaPiD: AI Accelerator for Ultra-low Precision Training and Inference.

Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Wang, Sanchari Sen, Jintao Zhang, Ankur Agrawal, Monodeep Kar, Shubham Jain, Alberto Mannari, Hoang Tran, Yulong Li, Eri Ogawa, Kazuaki Ishizaki, Hiroshi Inoue, Marcel Schaal, Mauricio J. Serrano, Jungwook Choi, Xiao Sun, Naigang Wang, Chia-Yu Chen, Allison Allain, James Bonanno, Nianzheng Cao, Robert Casatuta, Matthew Cohen, Bruce M. Fleischer, Michael Guillorn, Howard Haynie, Jinwook Jung, Mingu Kang, Kyu-Hyoun Kim, Siyu Koswatta, Sae Kyu Lee, Martin Lutz, Silvia M. Mueller, Jinwook Oh, Ashish Ranjan, Zhibin Ren, Scot Rider, Kerstin Schelm, Michael Scheuermann, Joel Silberman, Jie Yang, Vidhi Zalani, Xin Zhang, Ching Zhou, Matthew M. Ziegler, Vinay Shah, Moriyoshi Ohara, Pong-Fei Lu, Brian W. Curran, Sunil Shukla, Leland Chang, Kailash Gopalakrishnan

Proceedings of the 48th ACM/IEEE Annual International Symposium on Computer Architecture, 2021

A Framework for Adaptive Deep Reinforcement Semantic Parsing of Unstructured Data.

Shubham Jain, Amy de Buitléir, Enda Fallon

Proceedings of the International Conference on Information and Communication Technology Convergence, 2021

Design Tools for Resistive Crossbar based Machine Learning Accelerators.

Indranil Chakraborty, Sourjya Roy, Shrihari Sridharan, Mustafa Fayez Ali, Aayush Ankit, Shubham Jain, Anand Raghunathan

Proceedings of the 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, 2021

2020

TiM-DNN: Ternary In-Memory Accelerator for Deep Neural Networks.

Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan

IEEE Trans. Very Large Scale Integr. Syst., 2020

CxDNN: Hardware-software Compensation Methods for Deep Neural Networks on Resistive Crossbar Systems.

Shubham Jain, Anand Raghunathan

ACM Trans. Embed. Comput. Syst., 2020

Efficient AI System Design With Cross-Layer Approximate Computing.

Swagath Venkataramani, Xiao Sun, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Mingu Kang, Ankur Agarwal, Jinwook Oh, Shubham Jain, Tina Babinsky, Nianzheng Cao, Thomas W. Fox, Bruce M. Fleischer, George Gristede, Michael Guillorn, Howard Haynie, Hiroshi Inoue, Kazuaki Ishizaki, Michael J. Klaiber, Shih-Hsien Lo, Gary W. Maier, Silvia M. Mueller, Michael Scheuermann, Eri Ogawa, Marcel Schaal, Mauricio J. Serrano, Joel Silberman, Christos Vezyrtzis, Wei Wang, Fanchieh Yee, Jintao Zhang, Matthew M. Ziegler, Ching Zhou, Moriyoshi Ohara, Pong-Fei Lu, Brian W. Curran, Sunil Shukla, Vijayalakshmi Srinivasan, Leland Chang, Kailash Gopalakrishnan

Proc. IEEE, 2020

Resistive Crossbars as Approximate Hardware Building Blocks for Machine Learning: Opportunities and Challenges.

Indranil Chakraborty, Mustafa Fayez Ali, Aayush Ankit, Shubham Jain, Sourjya Roy, Shrihari Sridharan, Amogh Agrawal, Anand Raghunathan, Kaushik Roy

Proc. IEEE, 2020

Ternary Compute-Enabled Memory using Ferroelectric Transistors for Accelerating Deep Neural Networks.

Sandeep Krishna Thirumala, Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan

Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020

Emerging Neural Workloads and Their Impact on Hardware.

David Brooks, Martin M. Frank, Tayfun Gokmen, Udit Gupta, Xiaobo Sharon Hu, Shubham Jain, Ann Franchesca Laguna, Michael T. Niemier, Ian O'Connor, Anand Raghunathan, Ashish Ranjan, Dayane Reis, Jacob R. Stevens, Carole-Jean Wu, Xunzhao Yin

Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020

2019

SparCE: Sparsity Aware General-Purpose Core Extensions to Accelerate Deep Neural Networks.

Sanchari Sen, Shubham Jain, Swagath Venkataramani, Anand Raghunathan

IEEE Trans. Computers, 2019

Neural network accelerator design with resistive crossbars: Opportunities and challenges.

Shubham Jain, Aayush Ankit, Indranil Chakraborty, Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch, Kaushik Roy, Anand Raghunathan

IBM J. Res. Dev., 2019

Valley-Coupled-Spintronic Non-Volatile Memories with Compute-In-Memory Support.

Sandeep Krishna Thirumala, Yi-Tse Hung, Shubham Jain, Arnab Raha, Niharika Thakuria, Vijay Raghunathan, Anand Raghunathan, Zhihong Chen, Sumeet Kumar Gupta

CoRR, 2019

Non-Volatile Memory utilizing Reconfigurable Ferroelectric Transistors to enable Differential Read and Energy-Efficient In-Memory Computation.

Sandeep Krishna Thirumala, Shubham Jain, Anand Raghunathan, Sumeet Kumar Gupta

Proceedings of the 2019 IEEE/ACM International Symposium on Low Power Electronics and Design, 2019

X-MANN: A Crossbar based Architecture for Memory Augmented Neural Networks.

Ashish Ranjan, Shubham Jain, Jacob R. Stevens, Dipankar Das, Bharat Kaul, Anand Raghunathan

Proceedings of the 56th Annual Design Automation Conference 2019, 2019

BiScaled-DNN: Quantizing Long-tailed Datastructures with Two Scale Factors for Deep Neural Networks.

Shubham Jain, Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Kailash Gopalakrishnan, Leland Chang

Proceedings of the 56th Annual Design Automation Conference 2019, 2019

Automatic Synthesis Techniques for Approximate Circuits.

Ashish Ranjan, Swagath Venkataramani, Shubham Jain, Younghoon Kim, Shankar Ganesh Ramasubramanian, Arnab Raha, Kaushik Roy, Anand Raghunathan

Proceedings of the Approximate Circuits, Methodologies and CAD., 2019

2018

Computing in Memory With Spin-Transfer Torque Magnetic RAM.

Shubham Jain, Ashish Ranjan, Kaushik Roy, Anand Raghunathan

IEEE Trans. Very Large Scale Integr. Syst., 2018

Rx-Caffe: Framework for evaluating and training Deep Neural Networks on Resistive Crossbars.

Shubham Jain, Abhronil Sengupta, Kaushik Roy, Anand Raghunathan

CoRR, 2018

Computing-in-memory with spintronics.

Shubham Jain, Sachin S. Sapatnekar, Jianping Wang, Kaushik Roy, Anand Raghunathan

Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition, 2018

Compensated-DNN: energy efficient low-precision deep neural networks by compensating quantization errors.

Shubham Jain, Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Pierce Chuang, Leland Chang

Proceedings of the 55th Annual Design Automation Conference, 2018

2016

Approximation through logic isolation for the design of quality configurable circuits.

Shubham Jain, Swagath Venkataramani, Anand Raghunathan

Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition, 2016