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2020 – today
- 2024
[c60]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: 254-256- [i13]Zhenyu Liu, Garrett Gagnon, Swagath Venkataramani, Liu Liu:
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons. CoRR abs/2402.04325 (2024) - [i12]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 abs/2407.15866 (2024) - 2022
- [j13]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): 182-197 (2022) - [j12]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): 86:1-86:29 (2022) - [c59]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: 80:1-80:9 - [c58]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: 2038-2042 - [c57]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 - [i11]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 abs/2206.07882 (2022) - [i10]Jörg Henkel, Hai Li, Anand Raghunathan, Mehdi B. Tahoori, Swagath Venkataramani, Xiaoxuan Yang, Georgios Zervakis:
Approximate Computing and the Efficient Machine Learning Expedition. CoRR abs/2210.00497 (2022) - 2021
- [c56]Younghoon Kim, Swagath Venkataramani, Sanchari Sen, Anand Raghunathan:
Value Similarity Extensions for Approximate Computing in General-Purpose Processors. DATE 2021: 481-486 - [c55]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: 2586-2590 - [c54]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: 153-166 - [c53]Sanchari Sen, Swagath Venkataramani, Anand Raghunathan:
Efficacy of Pruning in Ultra-Low Precision DNNs. ISLPED 2021: 1-6 - [c52]Subhankar Pal, Swagath Venkataramani, Viji Srinivasan, Kailash Gopalakrishnan:
Efficient Management of Scratch-Pad Memories in Deep Learning Accelerators. ISPASS 2021: 240-242 - [c51]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: 144-146 - [i9]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 abs/2104.11125 (2021) - [i8]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 abs/2108.12074 (2021) - 2020
- [j11]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): 2232-2250 (2020) - [j10]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): 2503-2515 (2020) - [j9]Sanjay Ganapathy, Swagath Venkataramani, Giridhur Sriraman, Balaraman Ravindran, Anand Raghunathan:
DyVEDeep: Dynamic Variable Effort Deep Neural Networks. ACM Trans. Embed. Comput. Syst. 19(3): 16:1-16:24 (2020) - [c50]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 - [c49]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 - [c48]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: 1-2
2010 – 2019
- 2019
- [j8]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): 102-111 (2019) - [j7]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): 912-925 (2019) - [c47]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: 1-3 - [c46]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: 201 - [c45]Younghoon Kim, Swagath Venkataramani, Nitin Chandrachoodan, Anand Raghunathan:
Data Subsetting: A Data-Centric Approach to Approximate Computing. DATE 2019: 576-581 - [c44]Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Philip Heidelberger, Leland Chang, Kailash Gopalakrishnan:
Memory and Interconnect Optimizations for Peta-Scale Deep Learning Systems. HiPC 2019: 225-234 - [c43]Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung:
Workload-aware Automatic Parallelization for Multi-GPU DNN Training. ICASSP 2019: 1453-1457 - [c42]Swagath Venkataramani, Jungwook Choi, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, Leland Chang:
Performance-driven Programming of Multi-TFLOP Deep Learning Accelerators. IISWC 2019: 257-262 - [c41]Sarada Krithivasan, Sanchari Sen, Swagath Venkataramani, Anand Raghunathan:
Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks. ISLPED 2019: 1-6 - [c40]Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, Zhuo Wang, Pierce Chuang:
Accurate and Efficient 2-bit Quantized Neural Networks. SysML 2019 - [c39]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: 4901-4910 - [p3]Ashish Ranjan, Swagath Venkataramani, Shubham Jain, Younghoon Kim, Shankar Ganesh Ramasubramanian, Arnab Raha, Kaushik Roy, Anand Raghunathan:
Automatic Synthesis Techniques for Approximate Circuits. Approximate Circuits 2019: 123-140 - [p2]Jungwook Choi, Swagath Venkataramani:
Approximate Computing Techniques for Deep Neural Networks. Approximate Circuits 2019: 307-329 - 2018
- [j6]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): 16:1-16:23 (2018) - [c38]Mateja Putic, Swagath Venkataramani, Schuyler Eldridge, Alper Buyuktosunoglu, Pradip Bose, Mircea Stan:
Dyhard-DNN: even more DNN acceleration with dynamic hardware reconfiguration. DAC 2018: 14:1-14:6 - [c37]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: 38:1-38:6 - [c36]Chia-Yu Chen, Jungwook Choi, Kailash Gopalakrishnan, Viji Srinivasan, Swagath Venkataramani:
Exploiting approximate computing for deep learning acceleration. DATE 2018: 821-826 - [c35]Swagath Venkataramani, Vijayalakshmi Srinivasan, Jungwook Choi, Kailash Gopalakrishnan, Leland Chang:
Taming the beast: Programming Peta-FLOP class Deep Learning Systems. ISLPED 2018: 18:1 - [c34]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: 35:1-35:2 - [c33]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: 35-36 - [i7]Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan:
PACT: Parameterized Clipping Activation for Quantized Neural Networks. CoRR abs/1805.06085 (2018) - [i6]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 abs/1807.06964 (2018) - [i5]Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung:
Workload-aware Automatic Parallelization for Multi-GPU DNN Training. CoRR abs/1811.01532 (2018) - 2017
- [j5]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): 462-475 (2017) - [j4]Neel Gala, Swagath Venkataramani, Anand Raghunathan, V. Kamakoti:
Approximate Error Detection With Stochastic Checkers. IEEE Trans. Very Large Scale Integr. Syst. 25(8): 2258-2270 (2017) - [j3]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): 2673-2677 (2017) - [c32]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: 146-147 - [c31]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: 57:1-57:2 - [c30]Sanchari Sen, Swagath Venkataramani, Anand Raghunathan:
Approximate computing for spiking neural networks. DATE 2017: 193-198 - [c29]Ashish Ranjan, Swagath Venkataramani, Zoha Pajouhi, Rangharajan Venkatesan, Kaushik Roy, Anand Raghunathan:
STAxCache: An approximate, energy efficient STT-MRAM cache. DATE 2017: 356-361 - [c28]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: 601-604 - [c27]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: 13-26 - [c26]Arnab Roy, Swagath Venkataramani, Neel Gala, Sanchari Sen, Kamakoti Veezhinathan, Anand Raghunathan:
A Programmable Event-driven Architecture for Evaluating Spiking Neural Networks. ISLPED 2017: 1-6 - [i4]Sanjay Ganapathy, Swagath Venkataramani, Balaraman Ravindran, Anand Raghunathan:
DyVEDeep: Dynamic Variable Effort Deep Neural Networks. CoRR abs/1704.01137 (2017) - [i3]Sanchari Sen, Shubham Jain, Swagath Venkataramani, Anand Raghunathan:
SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks. CoRR abs/1711.06315 (2017) - 2016
- [b1]Swagath Venkataramani:
Approximate computing: An integrated cross-layer framework. Purdue University, USA, 2016 - [j2]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): 3243-3256 (2016) - [c25]Swagath Venkataramani, Kaushik Roy, Anand Raghunathan:
Efficient embedded learning for IoT devices. ASP-DAC 2016: 308-311 - [c24]Younghoon Kim, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan:
Designing approximate circuits using clock overgating. DAC 2016: 15:1-15:6 - [c23]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: 98:1-98:6 - [c22]Syed Shakib Sarwar, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy:
Multiplier-less Artificial Neurons exploiting error resiliency for energy-efficient neural computing. DATE 2016: 145-150 - [c21]Shubham Jain, Swagath Venkataramani, Anand Raghunathan:
Approximation through logic isolation for the design of quality configurable circuits. DATE 2016: 612-617 - [c20]Neel Gala, Swagath Venkataramani, Anand Raghunathan, V. Kamakoti:
STOCK: Stochastic Checkers for Low-overhead Approximate Error Detection. ISLPED 2016: 266-271 - [c19]Swagath Venkataramani, Kaushik Roy, Anand Raghunathan:
Approximate Computing. VLSID 2016: 3-4 - [i2]Syed Shakib Sarwar, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy:
Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing. CoRR abs/1602.08557 (2016) - 2015
- [j1]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): 1441-1454 (2015) - [c18]Swagath Venkataramani, Anand Raghunathan, Jie Liu, Mohammed Shoaib:
Scalable-effort classifiers for energy-efficient machine learning. DAC 2015: 67:1-67:6 - [c17]Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan:
Approximate computing and the quest for computing efficiency. DAC 2015: 120:1-120:6 - [c16]Ashish Ranjan, Swagath Venkataramani, Xuanyao Fong, Kaushik Roy, Anand Raghunathan:
Approximate storage for energy efficient spintronic memories. DAC 2015: 195:1-195:6 - [c15]Arnab Raha, Swagath Venkataramani, Vijay Raghunathan, Anand Raghunathan:
Quality configurable reduce-and-rank for energy efficient approximate computing. DATE 2015: 665-670 - [c14]Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan:
Computing approximately, and efficiently. DATE 2015: 748-751 - [c13]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: 1491-1496 - [c12]Rangharajan Venkatesan, Swagath Venkataramani, Xuanyao Fong, Kaushik Roy, Anand Raghunathan:
Spintastic: <u>spin</u>-based s<u>t</u>och<u>astic</u> logic for energy-efficient computing. DATE 2015: 1575-1578 - [p1]Mohammed Shoaib, Swagath Venkataramani, Xian-Sheng Hua, Jie Liu, Jin Li:
Exploiting On-Device Image Classification for Energy Efficiency in Ambient-Aware Systems. Mobile Cloud Visual Media Computing 2015: 167-199 - [i1]Priyadarshini Panda, Abhronil Sengupta, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy:
Object Detection using Semantic Decomposition for Energy-Efficient Neural Computing. CoRR abs/1509.08970 (2015) - 2014
- [c11]Ashish Ranjan, Arnab Raha, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan:
ASLAN: Synthesis of approximate sequential circuits. DATE 2014: 1-6 - [c10]Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan:
Approximate computing for efficient information processing. ESTIMedia 2014: 9-10 - [c9]Rangharajan Venkatesan, Shankar Ganesh Ramasubramanian, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan:
STAG: Spintronic-Tape Architecture for GPGPU cache hierarchies. ISCA 2014: 253-264 - [c8]Swagath Venkataramani, Ashish Ranjan, Kaushik Roy, Anand Raghunathan:
AxNN: energy-efficient neuromorphic systems using approximate computing. ISLPED 2014: 27-32 - [c7]Vinay K. Chippa, Swagath Venkataramani, Kaushik Roy, Anand Raghunathan:
StoRM: a stochastic recognition and mining processor. ISLPED 2014: 39-44 - [c6]Vivek Joy Kozhikkottu, Swagath Venkataramani, Sujit Dey, Anand Raghunathan:
Variation tolerant design of a vector processor for recognition, mining and synthesis. ISLPED 2014: 239-244 - 2013
- [c5]Vinay K. Chippa, Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan:
Approximate computing: An integrated hardware approach. ACSSC 2013: 111-117 - [c4]Shankar Ganesh Ramasubramanian, Swagath Venkataramani, Adithya Parandhaman, Anand Raghunathan:
Relax-and-retime: a methodology for energy-efficient recovery based design. DAC 2013: 111:1-111:6 - [c3]Swagath Venkataramani, Kaushik Roy, Anand Raghunathan:
Substitute-and-simplify: a unified design paradigm for approximate and quality configurable circuits. DATE 2013: 1367-1372 - [c2]Swagath Venkataramani, Vinay K. Chippa, Srimat T. Chakradhar, Kaushik Roy, Anand Raghunathan:
Quality programmable vector processors for approximate computing. MICRO 2013: 1-12 - 2012
- [c1]Swagath Venkataramani, Amit Sabne, Vivek Joy Kozhikkottu, Kaushik Roy, Anand Raghunathan:
SALSA: systematic logic synthesis of approximate circuits. DAC 2012: 796-801
Coauthor Index
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