Shubham Jain

39 papers A* 4A 7C 1Journal 22Unranked 4
YearRankTypeTitle / Venue / Authors
2026 A conf
Eliminating Redundancy: Ultra-compact Code Generation for Programmable Dataflow Accelerators.
CGO
Prasanth Chatarasi, Alex Gatea, Bardia Mahjour, Jintao Zhang, Alberto Mannari, Chris Bowler, Shubham Jain, Masoud Ataei Jaliseh, Nicole Khoun, Kamlesh Kumar, Viji Srinivasan, Swagath Venkataramani
2026 A conf
Enabling Spill-Free Compilation via Affine-Based Live Range Reduction Optimization.
CGO
Prasanth Chatarasi, Alex Gatea, Wei Wang, Chris Bowler, Shubham Jain, Masoud Ataei Jaliseh, Nicole Khoun, Alberto Mannari, Bardia Mahjour, Viji Srinivasan, Swagath Venkataramani
2026 — conf
Spyre: An Inference-Optimized Scalable AI Accelerator for Enterprise Workloads.
ISSCC
Matthew Cohen, Monodeep Kar, Swagath Venkataramani, Viji Srinivasan, Brian Veraa, Matthew M. Ziegler, Nianzheng Cao, Ashish Ranjan, Joel Silberman, Michael Guillorn, Sandra Woodward, JohnDavid Lancaster, Josh Hursey, Kyu-Hyoun Kim, Alberto Mannari, Amrit Nagarajan, Ananda Samajdar, Bahman Hekmatshoartabari, Bob Galbraith, Ching Zhou, David L. Satterfield, Gregory S. Still, Gustavo Tellez, Indira Nair, Indu Masilamanai, Jinwook Jung, Kavi Randhawa, Marcel Schaal, Martin Lutz, Paul Crumley, Philip Jacob, Prasanth Chatarasi, Radhika Jain, Sae Kyu Lee, Sanchari Sen, Sarada Krithivasan, Scot Rider, Shubham Jain, Siyu Koswatta, Thomas Roewer, Tom Gooding, Victor Ferrari, Vidhi Zalani, Zhibin Ren, Kevin Reick, Lisa Maurice, Christopher J. Gonzalez, Chris Catalino, Ryan Nett, Pong-Fei Lu, Robert M. Senger, Leland Chang
2025 J jnl
CiMBA: Accelerating Genome Sequencing Through On-Device Basecalling via Compute-in-Memory.
IEEE Trans. Parallel Distributed Syst.
William Andrew Simon, Irem Boybat, Riselda Kodra, Elena Ferro, Gagandeep Singh, Mohammed Alser, Shubham Jain, Hsinyu Tsai, Geoffrey W. Burr, Onur Mutlu, Abu Sebastian
2025 J jnl
CiMBA: Accelerating Genome Sequencing through On-Device Basecalling via Compute-in-Memory.
CoRR
William Andrew Simon, Irem Boybat, Riselda Kodra, Elena Ferro, Gagandeep Singh, Mohammed Alser, Shubham Jain, Hsinyu Tsai, Geoffrey W. Burr, Onur Mutlu, Abu Sebastian
2025 J jnl
Power-Limited Inference Performance Optimization Using a Software-Assisted Peak Current Regulation Scheme in a 5-nm AI SoC.
IEEE J. Solid State Circuits
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
2024 — conf
14.1 A Software-Assisted Peak Current Regulation Scheme to Improve Power-Limited Inference Performance in a 5nm AI SoC.
ISSCC
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
2024 J jnl
DNNDaSher: A Compiler Framework for Dataflow Compatible End-to-End Acceleration on IBM AIU.
IEEE Micro
Sanchari Sen, Shubham Jain, Sarada Krithivasan, Swagath Venkataramani, Vijayalakshmi Srinivasan
2023 J jnl
A Heterogeneous and Programmable Compute-In-Memory Accelerator Architecture for Analog-AI Using Dense 2-D Mesh.
IEEE Trans. Very Large Scale Integr. Syst.
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
2023 C conf
Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited).
ISCAS
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
2023 — conf
Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited).
IRPS
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
2022 J jnl
Ax-BxP: Approximate Blocked Computation for Precision-reconfigurable Deep Neural Network Acceleration.
ACM Trans. Design Autom. Electr. Syst.
Reena Elangovan, Shubham Jain, Anand Raghunathan
2021 — conf
Design Tools for Resistive Crossbar based Machine Learning Accelerators.
AICAS
Indranil Chakraborty, Sourjya Roy, Shrihari Sridharan, Mustafa Fayez Ali, Aayush Ankit, Shubham Jain, Anand Raghunathan
2021 A* conf
RaPiD: AI Accelerator for Ultra-low Precision Training and Inference.
ISCA
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
2021 J jnl
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.
Shubham Jain, Abhronil Sengupta, Kaushik Roy, Anand Raghunathan
2021 J jnl
TxSim: Modeling Training of Deep Neural Networks on Resistive Crossbar Systems.
IEEE Trans. Very Large Scale Integr. Syst.
Sourjya Roy, Shrihari Sridharan, Shubham Jain, Anand Raghunathan
2020 J jnl
Ax-BxP: Approximate Blocked Computation for Precision-Reconfigurable Deep Neural Network Acceleration.
CoRR
Reena Elangovan, Shubham Jain, Anand Raghunathan
2020 J jnl
CxDNN: Hardware-software Compensation Methods for Deep Neural Networks on Resistive Crossbar Systems.
ACM Trans. Embed. Comput. Syst.
Shubham Jain, Anand Raghunathan
2020 J jnl
Efficient AI System Design With Cross-Layer Approximate Computing.
Proc. IEEE
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
2020 A conf
Emerging Neural Workloads and Their Impact on Hardware.
DATE
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
2020 J jnl
Resistive Crossbars as Approximate Hardware Building Blocks for Machine Learning: Opportunities and Challenges.
Proc. IEEE
Indranil Chakraborty, Mustafa Fayez Ali, Aayush Ankit, Shubham Jain, Sourjya Roy, Shrihari Sridharan, Amogh Agrawal, Anand Raghunathan, Kaushik Roy
2020 A conf
Ternary Compute-Enabled Memory using Ferroelectric Transistors for Accelerating Deep Neural Networks.
DATE
Sandeep Krishna Thirumala, Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan
2020 J jnl
TiM-DNN: Ternary In-Memory Accelerator for Deep Neural Networks.
IEEE Trans. Very Large Scale Integr. Syst.
Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan
2020 J jnl
TxSim: Modeling Training of Deep Neural Networks on Resistive Crossbar Systems.
CoRR
Sourjya Roy, Shrihari Sridharan, Shubham Jain, Anand Raghunathan
2019 — ch.
Automatic Synthesis Techniques for Approximate Circuits.
Approximate Circuits
Ashish Ranjan, Swagath Venkataramani, Shubham Jain, Younghoon Kim, Shankar Ganesh Ramasubramanian, Arnab Raha, Kaushik Roy, Anand Raghunathan
2019 A* conf
BiScaled-DNN: Quantizing Long-tailed Datastructures with Two Scale Factors for Deep Neural Networks.
DAC
Shubham Jain, Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Kailash Gopalakrishnan, Leland Chang
2019 J jnl
Neural network accelerator design with resistive crossbars: Opportunities and challenges.
IBM J. Res. Dev.
Shubham Jain, Aayush Ankit, Indranil Chakraborty, Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch, Kaushik Roy, Anand Raghunathan
2019 A conf
Non-Volatile Memory utilizing Reconfigurable Ferroelectric Transistors to enable Differential Read and Energy-Efficient In-Memory Computation.
ISLPED
Sandeep Krishna Thirumala, Shubham Jain, Anand Raghunathan, Sumeet Kumar Gupta
2019 J jnl
SparCE: Sparsity Aware General-Purpose Core Extensions to Accelerate Deep Neural Networks.
IEEE Trans. Computers
Sanchari Sen, Shubham Jain, Swagath Venkataramani, Anand Raghunathan
2019 J jnl
TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks.
CoRR
Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan
2019 J jnl
Valley-Coupled-Spintronic Non-Volatile Memories with Compute-In-Memory Support.
CoRR
Sandeep Krishna Thirumala, Yi-Tse Hung, Shubham Jain, Arnab Raha, Niharika Thakuria, Vijay Raghunathan, Anand Raghunathan, Zhihong Chen, Sumeet Kumar Gupta
2019 A* conf
X-MANN: A Crossbar based Architecture for Memory Augmented Neural Networks.
DAC
Ashish Ranjan, Shubham Jain, Jacob R. Stevens, Dipankar Das, Bharat Kaul, Anand Raghunathan
2018 A* conf
Compensated-DNN: energy efficient low-precision deep neural networks by compensating quantization errors.
DAC
Shubham Jain, Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Pierce Chuang, Leland Chang
2018 J jnl
Computing in Memory With Spin-Transfer Torque Magnetic RAM.
IEEE Trans. Very Large Scale Integr. Syst.
Shubham Jain, Ashish Ranjan, Kaushik Roy, Anand Raghunathan
2018 A conf
Computing-in-memory with spintronics.
DATE
Shubham Jain, Sachin S. Sapatnekar, Jianping Wang, Kaushik Roy, Anand Raghunathan
2018 J jnl
Rx-Caffe: Framework for evaluating and training Deep Neural Networks on Resistive Crossbars.
CoRR
Shubham Jain, Abhronil Sengupta, Kaushik Roy, Anand Raghunathan
2017 J jnl
Computing in Memory with Spin-Transfer Torque Magnetic RAM.
CoRR
Shubham Jain, Ashish Ranjan, Kaushik Roy, Anand Raghunathan
2017 J jnl
SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks.
CoRR
Sanchari Sen, Shubham Jain, Swagath Venkataramani, Anand Raghunathan
2016 A conf
Approximation through logic isolation for the design of quality configurable circuits.
DATE
Shubham Jain, Swagath Venkataramani, Anand Raghunathan