Swagath Venkataramani

According to our database, Swagath Venkataramani authored at least 84 papers between 2012 and 2024.

Collaborative distances:

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

2024

MixTrain: accelerating DNN training via input mixing.

Sarada Krithivasan, Sanchari Sen, Swagath Venkataramani, Anand Raghunathan

Frontiers Artif. Intell., 2024

SmartQuant: CXL-based AI Model Store in Support of Runtime Configurable Weight Quantization.

Rui Xie, Asad Ul Haq, Linsen Ma, Krystal Sun, Sanchari Sen, Swagath Venkataramani, Liu Liu, Tong Zhang

CoRR, 2024

Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons.

Zhenyu Liu, Garrett Gagnon, Swagath Venkataramani, Liu Liu

CoRR, 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

2022

OnSRAM: Efficient Inter-Node On-Chip Scratchpad Management in Deep Learning Accelerators.

Subhankar Pal, Swagath Venkataramani, Viji Srinivasan, Kailash Gopalakrishnan

ACM Trans. Embed. Comput. Syst., November, 2022

A 7-nm Four-Core Mixed-Precision AI Chip With 26.2-TFLOPS Hybrid-FP8 Training, 104.9-TOPS INT4 Inference, and Workload-Aware Throttling.

Sae Kyu Lee, Ankur Agrawal, Joel Silberman, Matthew M. Ziegler, Mingu Kang, Swagath Venkataramani, Nianzheng Cao, Bruce M. Fleischer, Michael Guillorn, Matthew Cohen, Silvia M. Mueller, Jinwook Oh, Martin Lutz, Jinwook Jung, Siyu Koswatta, Ching Zhou, Vidhi Zalani, Monodeep Kar, James Bonanno, Robert Casatuta, Chia-Yu Chen, Jungwook Choi, Howard Haynie, Alyssa Herbert, Radhika Jain, Kyu-Hyoun Kim, Yulong Li, Zhibin Ren, Scot Rider, Marcel Schaal, Kerstin Schelm, Michael Scheuermann, Xiao Sun, Hung Tran, Naigang Wang, Wei Wang, Xin Zhang, Vinay Shah, Brian W. Curran, Vijayalakshmi Srinivasan, Pong-Fei Lu, Sunil Shukla, Kailash Gopalakrishnan, Leland Chang

IEEE J. Solid State Circuits, 2022

Deep Compression of Pre-trained Transformer Models.

Naigang Wang, Chi-Chun (Charlie) Liu, Swagath Venkataramani, Sanchari Sen, Chia-Yu Chen, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Leland Chang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization.

Andrea Fasoli, Chia-Yu Chen, Mauricio J. Serrano, Swagath Venkataramani, George Saon, Xiaodong Cui, Brian Kingsbury, Kailash Gopalakrishnan

Proceedings of the 23rd Annual Conference of the International Speech Communication Association, 2022

Approximate Computing and the Efficient Machine Learning Expedition.

Jörg Henkel, Hai Li, Anand Raghunathan, Mehdi B. Tahoori, Swagath Venkataramani, Xiaoxuan Yang, Georgios Zervakis

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, 2022

2021

A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling.

Ankur Agrawal, Sae Kyu Lee, Joel Silberman, Matthew M. Ziegler, Mingu Kang, Swagath Venkataramani, Nianzheng Cao, Bruce M. Fleischer, Michael Guillorn, Matt Cohen, Silvia M. Mueller, Jinwook Oh, Martin Lutz, Jinwook Jung, Siyu Koswatta, Ching Zhou, Vidhi Zalani, James Bonanno, Robert Casatuta, Chia-Yu Chen, Jungwook Choi, Howard Haynie, Alyssa Herbert, Radhika Jain, Monodeep Kar, Kyu-Hyoun Kim, Yulong Li, Zhibin Ren, Scot Rider, Marcel Schaal, Kerstin Schelm, Michael Scheuermann, Xiao Sun, Hung Tran, Naigang Wang, Wei Wang, Xin Zhang, Vinay Shah, Brian W. Curran, Vijayalakshmi Srinivasan, Pong-Fei Lu, Sunil Shukla, Leland Chang, Kailash Gopalakrishnan

Proceedings of the IEEE International Solid-State Circuits Conference, 2021

Efficient Management of Scratch-Pad Memories in Deep Learning Accelerators.

Subhankar Pal, Swagath Venkataramani, Viji Srinivasan, Kailash Gopalakrishnan

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2021

Efficacy of Pruning in Ultra-Low Precision DNNs.

Sanchari Sen, Swagath Venkataramani, Anand Raghunathan

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

4-Bit Quantization of LSTM-Based Speech Recognition Models.

Andrea Fasoli, Chia-Yu Chen, Mauricio J. Serrano, Xiao Sun, Naigang Wang, Swagath Venkataramani, George Saon, Xiaodong Cui, Brian Kingsbury, Wei Zhang, Zoltán Tüske, Kailash Gopalakrishnan

Proceedings of the 22nd Annual Conference of the International Speech Communication Association, Interspeech 2021, Brno, Czechia, August 30, 2021

Value Similarity Extensions for Approximate Computing in General-Purpose Processors.

Younghoon Kim, Swagath Venkataramani, Sanchari Sen, Anand Raghunathan

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021

2020

DyVEDeep: Dynamic Variable Effort Deep Neural Networks.

Sanjay Ganapathy, Swagath Venkataramani, Giridhur Sriraman, Balaraman Ravindran, Anand Raghunathan

ACM Trans. Embed. Comput. Syst., 2020

Logic Synthesis of Approximate Circuits.

Swagath Venkataramani, Vivek Joy Kozhikkottu, Amit Sabne, Kaushik Roy, Anand Raghunathan

IEEE Trans. Comput. Aided Des. Integr. Circuits 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

A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference.

Jinwook Oh, Sae Kyu Lee, Mingu Kang, Matthew M. Ziegler, Joel Silberman, Ankur Agrawal, Swagath Venkataramani, Bruce M. Fleischer, Michael Guillorn, Jungwook Choi, Wei Wang, Silvia M. Mueller, Shimon Ben-Yehuda, James Bonanno, Nianzheng Cao, Robert Casatuta, Chia-Yu Chen, Matt Cohen, Ophir Erez, Thomas W. Fox, George Gristede, Howard Haynie, Vicktoria Ivanov, Siyu Koswatta, Shih-Hsien Lo, Martin Lutz, Gary W. Maier, Alex Mesh, Yevgeny Nustov, Scot Rider, Marcel Schaal, Michael Scheuermann, Xiao Sun, Naigang Wang, Fanchieh Yee, Ching Zhou, Vinay Shah, Brian W. Curran, Vijayalakshmi Srinivasan, Pong-Fei Lu, Sunil Shukla, Kailash Gopalakrishnan, Leland Chang

Proceedings of the IEEE Symposium on VLSI Circuits, 2020

Ultra-Low Precision 4-bit Training of Deep Neural Networks.

Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training.

Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Zhang, Kailash Gopalakrishnan

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 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

DeepTools: Compiler and Execution Runtime Extensions for RaPiD AI Accelerator.

Swagath Venkataramani, Jungwook Choi, Vijayalakshmi Srinivasan, Wei Wang, Jintao Zhang, Marcel Schaal, Mauricio J. Serrano, Kazuaki Ishizaki, Hiroshi Inoue, Eri Ogawa, Moriyoshi Ohara, Leland Chang, Kailash Gopalakrishnan

IEEE Micro, 2019

Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks.

Xiao Sun, Jungwook Choi, Chia-Yu Chen, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Xiaodong Cui, Wei Zhang, Kailash Gopalakrishnan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Accurate and Efficient 2-bit Quantized Neural Networks.

Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, Zhuo Wang, Pierce Chuang

Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, 2019

Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks.

Sarada Krithivasan, Sanchari Sen, Swagath Venkataramani, Anand Raghunathan

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

Performance-driven Programming of Multi-TFLOP Deep Learning Accelerators.

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

Proceedings of the IEEE International Symposium on Workload Characterization, 2019

Workload-aware Automatic Parallelization for Multi-GPU DNN Training.

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung

Proceedings of the IEEE International Conference on Acoustics, 2019

Memory and Interconnect Optimizations for Peta-Scale Deep Learning Systems.

Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Philip Heidelberger, Leland Chang, Kailash Gopalakrishnan

Proceedings of the 26th IEEE International Conference on High Performance Computing, 2019

Data Subsetting: A Data-Centric Approach to Approximate Computing.

Younghoon Kim, Swagath Venkataramani, Nitin Chandrachoodan, Anand Raghunathan

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

A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation Kernel.

Eri Ogawa, Kazuaki Ishizaki, Hiroshi Inoue, Swagath Venkataramani, Jungwook Choi, Wei Wang, Vijayalakshmi Srinivasan, Moriyoshi Ohara, Kailash Gopalakrishnan

Proceedings of the IEEE Symposium in Low-Power and High-Speed Chips, 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

Approximate Computing Techniques for Deep Neural Networks.

Jungwook Choi, Swagath Venkataramani

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

2018

Energy-Efficient Neural Computing with Approximate Multipliers.

Syed Shakib Sarwar, Swagath Venkataramani, Aayush Ankit, Anand Raghunathan, Kaushik Roy

ACM J. Emerg. Technol. Comput. Syst., 2018

Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN).

Jungwook Choi, Pierce I-Jen Chuang, Zhuo Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan

CoRR, 2018

PACT: Parameterized Clipping Activation for Quantized Neural Networks.

Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan

CoRR, 2018

A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference.

Bruce M. Fleischer, Sunil Shukla, Matthew M. Ziegler, Joel Silberman, Jinwook Oh, Vijayalakshmi Srinivasan, Jungwook Choi, Silvia M. Mueller, Ankur Agrawal, Tina Babinsky, Nianzheng Cao, Chia-Yu Chen, Pierce Chuang, Thomas W. Fox, George Gristede, Michael Guillorn, Howard Haynie, Michael J. Klaiber, Dongsoo Lee, Shih-Hsien Lo, Gary W. Maier, Michael Scheuermann, Swagath Venkataramani, Christos Vezyrtzis, Naigang Wang, Fanchieh Yee, Ching Zhou, Pong-Fei Lu, Brian W. Curran, Leland Chang, Kailash Gopalakrishnan

Proceedings of the 2018 IEEE Symposium on VLSI Circuits, 2018

Taming the beast: Programming Peta-FLOP class Deep Learning Systems.

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

Proceedings of the International Symposium on Low Power Electronics and Design, 2018

Across the Stack Opportunities for Deep Learning Acceleration.

Vijayalakshmi Srinivasan, Bruce M. Fleischer, Sunil Shukla, Matthew M. Ziegler, Joel Silberman, Jinwook Oh, Jungwook Choi, Silvia M. Mueller, Ankur Agrawal, Tina Babinsky, Nianzheng Cao, Chia-Yu Chen, Pierce Chuang, Thomas W. Fox, George Gristede, Michael Guillorn, Howard Haynie, Michael J. Klaiber, Dongsoo Lee, Shih-Hsien Lo, Gary W. Maier, Michael Scheuermann, Swagath Venkataramani, Christos Vezyrtzis, Naigang Wang, Fanchieh Yee, Ching Zhou, Pong-Fei Lu, Brian W. Curran, Leland Chang, Kailash Gopalakrishnan

Proceedings of the International Symposium on Low Power Electronics and Design, 2018

Exploiting approximate computing for deep learning acceleration.

Chia-Yu Chen, Jungwook Choi, Kailash Gopalakrishnan, Viji Srinivasan, Swagath Venkataramani

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

Dyhard-DNN: even more DNN acceleration with dynamic hardware reconfiguration.

Mateja Putic, Swagath Venkataramani, Schuyler Eldridge, Alper Buyuktosunoglu, Pradip Bose, Mircea Stan

Proceedings of the 55th Annual Design Automation Conference, 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

2017

Energy-Efficient Reduce-and-Rank Using Input-Adaptive Approximations.

Arnab Raha, Swagath Venkataramani, Vijay Raghunathan, Anand Raghunathan

IEEE Trans. Very Large Scale Integr. Syst., 2017

Energy-Efficient Object Detection Using Semantic Decomposition.

Priyadarshini Panda, Swagath Venkataramani, Abhronil Sengupta, Anand Raghunathan, Kaushik Roy

IEEE Trans. Very Large Scale Integr. Syst., 2017

Approximate Error Detection With Stochastic Checkers.

Neel Gala, Swagath Venkataramani, Anand Raghunathan, V. Kamakoti

IEEE Trans. Very Large Scale Integr. Syst., 2017

DyVEDeep: Dynamic Variable Effort Deep Neural Networks.

Sanjay Ganapathy, Swagath Venkataramani, Balaraman Ravindran, Anand Raghunathan

CoRR, 2017

A Programmable Event-driven Architecture for Evaluating Spiking Neural Networks.

Arnab Roy, Swagath Venkataramani, Neel Gala, Sanchari Sen, Kamakoti Veezhinathan, Anand Raghunathan

Proceedings of the 2017 IEEE/ACM International Symposium on Low Power Electronics and Design, 2017

ScaleDeep: A Scalable Compute Architecture for Learning and Evaluating Deep Networks.

Swagath Venkataramani, Ashish Ranjan, Subarno Banerjee, Dipankar Das, Sasikanth Avancha, Ashok Jagannathan, Ajaya Durg, Dheemanth Nagaraj, Bharat Kaul, Pradeep Dubey, Anand Raghunathan

Proceedings of the 44th Annual International Symposium on Computer Architecture, 2017

Very Low Voltage (VLV) Design.

Ramon Bertran, Pradip Bose, David M. Brooks, Jeff Burns, Alper Buyuktosunoglu, Nandhini Chandramoorthy, Eric Cheng, Martin Cochet, Schuyler Eldridge, Daniel Friedman, Hans M. Jacobson, Rajiv V. Joshi, Subhasish Mitra, Robert K. Montoye, Arun Paidimarri, Pritish Parida, Kevin Skadron, Mircea Stan, Karthik Swaminathan, Augusto Vega, Swagath Venkataramani, Christos Vezyrtzis, Gu-Yeon Wei, John-David Wellman, Matthew M. Ziegler

Proceedings of the 2017 IEEE International Conference on Computer Design, 2017

Approximate computing for spiking neural networks.

Sanchari Sen, Swagath Venkataramani, Anand Raghunathan

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2017

STAxCache: An approximate, energy efficient STT-MRAM cache.

Ashish Ranjan, Swagath Venkataramani, Zoha Pajouhi, Rangharajan Venkatesan, Kaushik Roy, Anand Raghunathan

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2017

Accelerator Design for Deep Learning Training: Extended Abstract: Invited.

Ankur Agrawal, Chia-Yu Chen, Jungwook Choi, Kailash Gopalakrishnan, Jinwook Oh, Sunil Shukla, Viji Srinivasan, Swagath Venkataramani, Wei Zhang

Proceedings of the 54th Annual Design Automation Conference, 2017

POSTER: Design Space Exploration for Performance Optimization of Deep Neural Networks on Shared Memory Accelerators.

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

Proceedings of the 26th International Conference on Parallel Architectures and Compilation Techniques, 2017

2016

Approximate computing: An integrated cross-layer framework

Swagath Venkataramani

PhD thesis, 2016

EMBIRA: An Accelerator for Model-Based Iterative Reconstruction.

Junshi Liu, Swagath Venkataramani, Singanallur V. Venkatakrishnan, Yun Pan, Charles A. Bouman, Anand Raghunathan

IEEE Trans. Very Large Scale Integr. Syst., 2016

Approximate Computing.

Swagath Venkataramani, Kaushik Roy, Anand Raghunathan

Proceedings of the 29th International Conference on VLSI Design and 15th International Conference on Embedded Systems, 2016

STOCK: Stochastic Checkers for Low-overhead Approximate Error Detection.

Neel Gala, Swagath Venkataramani, Anand Raghunathan, V. Kamakoti

Proceedings of the 2016 International Symposium on Low Power Electronics and Design, 2016

Multiplier-less Artificial Neurons exploiting error resiliency for energy-efficient neural computing.

Syed Shakib Sarwar, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy

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

Invited - Cross-layer approximations for neuromorphic computing: from devices to circuits and systems.

Priyadarshini Panda, Abhronil Sengupta, Syed Shakib Sarwar, Gopalakrishnan Srinivasan, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy

Proceedings of the 53rd Annual Design Automation Conference, 2016

Designing approximate circuits using clock overgating.

Younghoon Kim, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan

Proceedings of the 53rd Annual Design Automation Conference, 2016

Efficient embedded learning for IoT devices.

Swagath Venkataramani, Kaushik Roy, Anand Raghunathan

Proceedings of the 21st Asia and South Pacific Design Automation Conference, 2016

2015

Exploring Spin-Transfer-Torque Devices for Logic Applications.

Zoha Pajouhi, Swagath Venkataramani, Karthik Yogendra, Anand Raghunathan, Kaushik Roy

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2015

Object Detection using Semantic Decomposition for Energy-Efficient Neural Computing.

Priyadarshini Panda, Abhronil Sengupta, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy

CoRR, 2015

Spintastic: <u>spin</u>-based s<u>t</u>och<u>astic</u> logic for energy-efficient computing.

Rangharajan Venkatesan, Swagath Venkataramani, Xuanyao Fong, Kaushik Roy, Anand Raghunathan

Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition, 2015

Computing approximately, and efficiently.

Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan

Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition, 2015

SAPPHIRE: an always-on context-aware computer vision system for portable devices.

Swagath Venkataramani, Victor Bahl, Xian-Sheng Hua, Jie Liu, Jin Li, Matthai Philipose, Bodhi Priyantha, Mohammed Shoaib

Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition, 2015

Quality configurable reduce-and-rank for energy efficient approximate computing.

Arnab Raha, Swagath Venkataramani, Vijay Raghunathan, Anand Raghunathan

Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition, 2015

Scalable-effort classifiers for energy-efficient machine learning.

Swagath Venkataramani, Anand Raghunathan, Jie Liu, Mohammed Shoaib

Proceedings of the 52nd Annual Design Automation Conference, 2015

Approximate computing and the quest for computing efficiency.

Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan

Proceedings of the 52nd Annual Design Automation Conference, 2015

Approximate storage for energy efficient spintronic memories.

Ashish Ranjan, Swagath Venkataramani, Xuanyao Fong, Kaushik Roy, Anand Raghunathan

Proceedings of the 52nd Annual Design Automation Conference, 2015

Exploiting On-Device Image Classification for Energy Efficiency in Ambient-Aware Systems.

Mohammed Shoaib, Swagath Venkataramani, Xian-Sheng Hua, Jie Liu, Jin Li

Proceedings of the Mobile Cloud Visual Media Computing - From Interaction to Service, 2015

2014

AxNN: energy-efficient neuromorphic systems using approximate computing.

Swagath Venkataramani, Ashish Ranjan, Kaushik Roy, Anand Raghunathan

Proceedings of the International Symposium on Low Power Electronics and Design, 2014

Variation tolerant design of a vector processor for recognition, mining and synthesis.

Vivek Joy Kozhikkottu, Swagath Venkataramani, Sujit Dey, Anand Raghunathan

Proceedings of the International Symposium on Low Power Electronics and Design, 2014

StoRM: a stochastic recognition and mining processor.

Vinay K. Chippa, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan

Proceedings of the International Symposium on Low Power Electronics and Design, 2014

STAG: Spintronic-Tape Architecture for GPGPU cache hierarchies.

Rangharajan Venkatesan, Shankar Ganesh Ramasubramanian, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan

Proceedings of the ACM/IEEE 41st International Symposium on Computer Architecture, 2014

Approximate computing for efficient information processing.

Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan

Proceedings of the 12th IEEE Symposium on Embedded Systems for Real-time Multimedia, 2014

ASLAN: Synthesis of approximate sequential circuits.

Ashish Ranjan, Arnab Raha, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2014

2013

Quality programmable vector processors for approximate computing.

Swagath Venkataramani, Vinay K. Chippa, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan

Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture, 2013

Substitute-and-simplify: a unified design paradigm for approximate and quality configurable circuits.

Swagath Venkataramani, Kaushik Roy, Anand Raghunathan

Proceedings of the Design, Automation and Test in Europe, 2013

Relax-and-retime: a methodology for energy-efficient recovery based design.

Shankar Ganesh Ramasubramanian, Swagath Venkataramani, Adithya Parandhaman, Anand Raghunathan

Proceedings of the 50th Annual Design Automation Conference 2013, 2013

Approximate computing: An integrated hardware approach.

Vinay K. Chippa, Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan

Proceedings of the 2013 Asilomar Conference on Signals, 2013

2012

SALSA: systematic logic synthesis of approximate circuits.

Swagath Venkataramani, Amit Sabne, Vivek Joy Kozhikkottu, Kaushik Roy, Anand Raghunathan

Proceedings of the 49th Annual Design Automation Conference 2012, 2012