Swagath Venkataramani

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 75

Top Topics

Iterative Reconstruction

Error Detection

Top Venues

Publications

Zhenyu Liu, Garrett Gagnon, Swagath Venkataramani, Liu Liu
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons. CoRR (2024)
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
14.1 A Software-Assisted Peak Current Regulation Scheme to Improve Power-Limited Inference Performance in a 5nm AI SoC. ISSCC (2024)
Rui Xie, Asad Ul Haq, Linsen Ma, Krystal Sun, Sanchari Sen, Swagath Venkataramani, Liu Liu, Tong Zhang
SmartQuant: CXL-based AI Model Store in Support of Runtime Configurable Weight Quantization. CoRR (2024)
Naigang Wang, Chi-Chun (Charlie) Liu, Swagath Venkataramani, Sanchari Sen, Chia-Yu Chen, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Leland Chang
Deep Compression of Pre-trained Transformer Models. NeurIPS (2022)
Jörg Henkel, Hai Li, Anand Raghunathan, Mehdi B. Tahoori, Swagath Venkataramani, Xiaoxuan Yang, Georgios Zervakis
Approximate Computing and the Efficient Machine Learning Expedition. ICCAD (2022)
Jörg Henkel, Hai Li, Anand Raghunathan, Mehdi B. Tahoori, Swagath Venkataramani, Xiaoxuan Yang, Georgios Zervakis
Approximate Computing and the Efficient Machine Learning Expedition. CoRR (2022)
Andrea Fasoli, Chia-Yu Chen, Mauricio J. Serrano, Swagath Venkataramani, George Saon, Xiaodong Cui, Brian Kingsbury, Kailash Gopalakrishnan
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization. INTERSPEECH (2022)
Andrea Fasoli, Chia-Yu Chen, Mauricio J. Serrano, Swagath Venkataramani, George Saon, Xiaodong Cui, Brian Kingsbury, Kailash Gopalakrishnan
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization. CoRR (2022)
Subhankar Pal, Swagath Venkataramani, Viji Srinivasan, Kailash Gopalakrishnan
OnSRAM: Efficient Inter-Node On-Chip Scratchpad Management in Deep Learning Accelerators. ACM Trans. Embed. Comput. Syst. 21 (6) (2022)
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
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. IEEE J. Solid State Circuits 57 (1) (2022)
Subhankar Pal, Swagath Venkataramani, Viji Srinivasan, Kailash Gopalakrishnan
Efficient Management of Scratch-Pad Memories in Deep Learning Accelerators. ISPASS (2021)
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
A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling. ISSCC (2021)
Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Zhang, Kailash Gopalakrishnan
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. CoRR (2021)
Younghoon Kim, Swagath Venkataramani, Sanchari Sen, Anand Raghunathan
Value Similarity Extensions for Approximate Computing in General-Purpose Processors. DATE (2021)
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
4-Bit Quantization of LSTM-Based Speech Recognition Models. Interspeech (2021)
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
RaPiD: AI Accelerator for Ultra-low Precision Training and Inference. ISCA (2021)
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
4-bit Quantization of LSTM-based Speech Recognition Models. CoRR (2021)
Sanchari Sen, Swagath Venkataramani, Anand Raghunathan
Efficacy of Pruning in Ultra-Low Precision DNNs. ISLPED (2021)
Sanjay Ganapathy, Swagath Venkataramani, Giridhur Sriraman, Balaraman Ravindran, Anand Raghunathan
DyVEDeep: Dynamic Variable Effort Deep Neural Networks. ACM Trans. Embed. Comput. Syst. 19 (3) (2020)
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
Efficient AI System Design With Cross-Layer Approximate Computing. Proc. IEEE 108 (12) (2020)
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
A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference. VLSI Circuits (2020)
Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan
Ultra-Low Precision 4-bit Training of Deep Neural Networks. NeurIPS (2020)
Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Zhang, Kailash Gopalakrishnan
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. NeurIPS (2020)
Swagath Venkataramani, Vivek Joy Kozhikkottu, Amit Sabne, Kaushik Roy, Anand Raghunathan
Logic Synthesis of Approximate Circuits. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 39 (10) (2020)
Shubham Jain, Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Kailash Gopalakrishnan, Leland Chang
BiScaled-DNN: Quantizing Long-tailed Datastructures with Two Scale Factors for Deep Neural Networks. DAC (2019)
Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, Zhuo Wang, Pierce Chuang
Accurate and Efficient 2-bit Quantized Neural Networks. MLSys (2019)
Eri Ogawa, Kazuaki Ishizaki, Hiroshi Inoue, Swagath Venkataramani, Jungwook Choi, Wei Wang, Vijayalakshmi Srinivasan, Moriyoshi Ohara, Kailash Gopalakrishnan
A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation Kernel. COOL CHIPS (2019)
Sanchari Sen, Shubham Jain, Swagath Venkataramani, Anand Raghunathan
SparCE: Sparsity Aware General-Purpose Core Extensions to Accelerate Deep Neural Networks. IEEE Trans. Computers 68 (6) (2019)
Younghoon Kim, Swagath Venkataramani, Nitin Chandrachoodan, Anand Raghunathan
Data Subsetting: A Data-Centric Approach to Approximate Computing. DATE (2019)
Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung
Workload-aware Automatic Parallelization for Multi-GPU DNN Training. ICASSP (2019)
Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Philip Heidelberger, Leland Chang, Kailash Gopalakrishnan
Memory and Interconnect Optimizations for Peta-Scale Deep Learning Systems. HiPC (2019)
Sarada Krithivasan, Sanchari Sen, Swagath Venkataramani, Anand Raghunathan
Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks. ISLPED (2019)
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
DeepTools: Compiler and Execution Runtime Extensions for RaPiD AI Accelerator. IEEE Micro 39 (5) (2019)
Xiao Sun, Jungwook Choi, Chia-Yu Chen, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Xiaodong Cui, Wei Zhang, Kailash Gopalakrishnan
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks. NeurIPS (2019)
Swagath Venkataramani, Jungwook Choi, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, Leland Chang
Performance-driven Programming of Multi-TFLOP Deep Learning Accelerators. IISWC (2019)
Shubham Jain, Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Pierce Chuang, Leland Chang
Compensated-DNN: energy efficient low-precision deep neural networks by compensating quantization errors. DAC (2018)
Jungwook Choi, Pierce I-Jen Chuang, Zhuo Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN). CoRR (2018)
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
A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference. VLSI Circuits (2018)
Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan
PACT: Parameterized Clipping Activation for Quantized Neural Networks. CoRR (2018)
Mateja Putic, Swagath Venkataramani, Schuyler Eldridge, Alper Buyuktosunoglu, Pradip Bose, Mircea Stan
Dyhard-DNN: even more DNN acceleration with dynamic hardware reconfiguration. DAC (2018)
Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Kailash Gopalakrishnan, Leland Chang
Taming the beast: Programming Peta-FLOP class Deep Learning Systems. ISLPED (2018)
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
Across the Stack Opportunities for Deep Learning Acceleration. ISLPED (2018)
Syed Shakib Sarwar, Swagath Venkataramani, Aayush Ankit, Anand Raghunathan, Kaushik Roy
Energy-Efficient Neural Computing with Approximate Multipliers. ACM J. Emerg. Technol. Comput. Syst. 14 (2) (2018)
Chia-Yu Chen, Jungwook Choi, Kailash Gopalakrishnan, Viji Srinivasan, Swagath Venkataramani
Exploiting approximate computing for deep learning acceleration. DATE (2018)
Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung
Workload-aware Automatic Parallelization for Multi-GPU DNN Training. CoRR (2018)
Ankur Agrawal, Chia-Yu Chen, Jungwook Choi, Kailash Gopalakrishnan, Jinwook Oh, Sunil Shukla, Viji Srinivasan, Swagath Venkataramani, Wei Zhang
Accelerator Design for Deep Learning Training: Extended Abstract: Invited. DAC (2017)
Swagath Venkataramani, Jungwook Choi, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, Leland Chang
POSTER: Design Space Exploration for Performance Optimization of Deep Neural Networks on Shared Memory Accelerators. PACT (2017)
Sanchari Sen, Shubham Jain, Swagath Venkataramani, Anand Raghunathan
SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks. CoRR (2017)
Neel Gala, Swagath Venkataramani, Anand Raghunathan, V. Kamakoti
Approximate Error Detection With Stochastic Checkers. IEEE Trans. Very Large Scale Integr. Syst. 25 (8) (2017)
Sanchari Sen, Swagath Venkataramani, Anand Raghunathan
Approximate computing for spiking neural networks. DATE (2017)
Ashish Ranjan, Swagath Venkataramani, Zoha Pajouhi, Rangharajan Venkatesan, Kaushik Roy, Anand Raghunathan
STAxCache: An approximate, energy efficient STT-MRAM cache. DATE (2017)
Arnab Roy, Swagath Venkataramani, Neel Gala, Sanchari Sen, Kamakoti Veezhinathan, Anand Raghunathan
A Programmable Event-driven Architecture for Evaluating Spiking Neural Networks. ISLPED (2017)
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
Very Low Voltage (VLV) Design. ICCD (2017)
Priyadarshini Panda, Swagath Venkataramani, Abhronil Sengupta, Anand Raghunathan, Kaushik Roy
Energy-Efficient Object Detection Using Semantic Decomposition. IEEE Trans. Very Large Scale Integr. Syst. 25 (9) (2017)
Sanjay Ganapathy, Swagath Venkataramani, Balaraman Ravindran, Anand Raghunathan
DyVEDeep: Dynamic Variable Effort Deep Neural Networks. CoRR (2017)
Swagath Venkataramani, Ashish Ranjan, Subarno Banerjee, Dipankar Das, Sasikanth Avancha, Ashok Jagannathan, Ajaya Durg, Dheemanth Nagaraj, Bharat Kaul, Pradeep Dubey, Anand Raghunathan
ScaleDeep: A Scalable Compute Architecture for Learning and Evaluating Deep Networks. ISCA (2017)
Arnab Raha, Swagath Venkataramani, Vijay Raghunathan, Anand Raghunathan
Energy-Efficient Reduce-and-Rank Using Input-Adaptive Approximations. IEEE Trans. Very Large Scale Integr. Syst. 25 (2) (2017)
Shubham Jain, Swagath Venkataramani, Anand Raghunathan
Approximation through logic isolation for the design of quality configurable circuits. DATE (2016)
Swagath Venkataramani, Kaushik Roy, Anand Raghunathan
Efficient embedded learning for IoT devices. ASP-DAC (2016)
Junshi Liu, Swagath Venkataramani, Singanallur V. Venkatakrishnan, Yun Pan, Charles A. Bouman, Anand Raghunathan
EMBIRA: An Accelerator for Model-Based Iterative Reconstruction. IEEE Trans. Very Large Scale Integr. Syst. 24 (11) (2016)
Younghoon Kim, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan
Designing approximate circuits using clock overgating. DAC (2016)
Syed Shakib Sarwar, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy
Multiplier-less Artificial Neurons exploiting error resiliency for energy-efficient neural computing. DATE (2016)
Syed Shakib Sarwar, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy
Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing. CoRR (2016)
Priyadarshini Panda, Abhronil Sengupta, Syed Shakib Sarwar, Gopalakrishnan Srinivasan, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy
Invited - Cross-layer approximations for neuromorphic computing: from devices to circuits and systems. DAC (2016)
Swagath Venkataramani, Kaushik Roy, Anand Raghunathan
Approximate Computing. VLSI Design (2016)
Neel Gala, Swagath Venkataramani, Anand Raghunathan, V. Kamakoti
STOCK: Stochastic Checkers for Low-overhead Approximate Error Detection. ISLPED (2016)
Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan
Approximate computing and the quest for computing efficiency. DAC (2015)
Arnab Raha, Swagath Venkataramani, Vijay Raghunathan, Anand Raghunathan
Quality configurable reduce-and-rank for energy efficient approximate computing. DATE (2015)
Rangharajan Venkatesan, Swagath Venkataramani, Xuanyao Fong, Kaushik Roy, Anand Raghunathan
Spintastic: spin-based stochastic logic for energy-efficient computing. DATE (2015)
Priyadarshini Panda, Abhronil Sengupta, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy
Object Detection using Semantic Decomposition for Energy-Efficient Neural Computing. CoRR (2015)
Zoha Pajouhi, Swagath Venkataramani, Karthik Yogendra, Anand Raghunathan, Kaushik Roy
Exploring Spin-Transfer-Torque Devices for Logic Applications. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 34 (9) (2015)
Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan
Computing approximately, and efficiently. DATE (2015)
Swagath Venkataramani, Anand Raghunathan, Jie Liu, Mohammed Shoaib
Scalable-effort classifiers for energy-efficient machine learning. DAC (2015)
Swagath Venkataramani, Victor Bahl, Xian-Sheng Hua, Jie Liu, Jin Li, Matthai Philipose, Bodhi Priyantha, Mohammed Shoaib
SAPPHIRE: an always-on context-aware computer vision system for portable devices. DATE (2015)
Ashish Ranjan, Swagath Venkataramani, Xuanyao Fong, Kaushik Roy, Anand Raghunathan
Approximate storage for energy efficient spintronic memories. DAC (2015)
Vivek Joy Kozhikkottu, Swagath Venkataramani, Sujit Dey, Anand Raghunathan
Variation tolerant design of a vector processor for recognition, mining and synthesis. ISLPED (2014)
Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan
Approximate computing for efficient information processing. ESTIMedia (2014)
Vinay K. Chippa, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan
StoRM: a stochastic recognition and mining processor. ISLPED (2014)
Rangharajan Venkatesan, Shankar Ganesh Ramasubramanian, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan
STAG: Spintronic-Tape Architecture for GPGPU cache hierarchies. ISCA (2014)
Ashish Ranjan, Arnab Raha, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan
ASLAN: Synthesis of approximate sequential circuits. DATE (2014)
Swagath Venkataramani, Ashish Ranjan, Kaushik Roy, Anand Raghunathan
AxNN: energy-efficient neuromorphic systems using approximate computing. ISLPED (2014)
Vinay K. Chippa, Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan
Approximate computing: An integrated hardware approach. ACSSC (2013)
Swagath Venkataramani, Kaushik Roy, Anand Raghunathan
Substitute-and-simplify: a unified design paradigm for approximate and quality configurable circuits. DATE (2013)
Swagath Venkataramani, Vinay K. Chippa, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan
Quality programmable vector processors for approximate computing. MICRO (2013)
Shankar Ganesh Ramasubramanian, Swagath Venkataramani, Adithya Parandhaman, Anand Raghunathan
Relax-and-retime: a methodology for energy-efficient recovery based design. DAC (2013)
Swagath Venkataramani, Amit Sabne, Vivek Joy Kozhikkottu, Kaushik Roy, Anand Raghunathan
SALSA: systematic logic synthesis of approximate circuits. DAC (2012)