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Publication search results
found 87 matches
- 2024
- V. Bala Naga Jyothi, S. Jai Akash, G. Ananda Ramadass, N. Vedachalam, Hrishikesh Venkataraman:
Design and Development of Deep Learning-Aided Vision Guidance System for AUV Homing Applications. IEEE Embed. Syst. Lett. 16(2): 198-201 (2024) - 2023
- Yeshe M. Kway, Kashthuri Thirumurugan, Navin Michael, Kok Hian Tan, Keith M. Godfrey, Peter D. Gluckman, Yap-Seng Chong, Kavita Venkataraman, Eric Yin Hao Khoo, Chin Meng Khoo, Melvin Khee-Shing Leow, E. Shyong Tai, Jerry KY. Chan, Shiao-Yng Chan, Johan G. Eriksson, Marielle V. Fortier, Yung Seng Lee, S. Sendhil Velan, Mengling Feng, Suresh Anand Sadananthan:
A fully convolutional neural network for comprehensive compartmentalization of abdominal adipose tissue compartments in MRI. Comput. Biol. Medicine 167: 107608 (2023) - 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: 80:1-80:9 - Deepa Anand, Rohan Patil, Utkarsh Agrawal, Rahul Venkataramani, Hariharan Ravishankar, Prasad Sudhakar:
Towards Generalization of Medical Imaging AI Models: Sharpness-Aware Minimizers and Beyond. ISBI 2022: 1-5 - Hariharan Ravishankar, Rohan Patil, Deepa Anand, Vanika Singhal, Utkarsh Agrawal, Rahul Venkataramani, Prasad Sudhakar:
Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertainty. UNSURE@MICCAI 2022: 80-88 - 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
- Younghoon Kim, Swagath Venkataramani, Sanchari Sen, Anand Raghunathan:
Value Similarity Extensions for Approximate Computing in General-Purpose Processors. DATE 2021: 481-486 - Sanchari Sen, Swagath Venkataramani, Anand Raghunathan:
Efficacy of Pruning in Ultra-Low Precision DNNs. ISLPED 2021: 1-6 - 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): 2503-2515 (2020) - 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) - 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): 912-925 (2019) - Younghoon Kim, Swagath Venkataramani, Nitin Chandrachoodan, Anand Raghunathan:
Data Subsetting: A Data-Centric Approach to Approximate Computing. DATE 2019: 576-581 - Sarada Krithivasan, Sanchari Sen, Swagath Venkataramani, Anand Raghunathan:
Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks. ISLPED 2019: 1-6 - 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 - 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): 16:1-16:23 (2018) - Anand Padmanabha Iyer, Aurojit Panda, Shivaram Venkataraman, Mosharaf Chowdhury, Aditya Akella, Scott Shenker, Ion Stoica:
Bridging the GAP: towards approximate graph analytics. GRADES/NDA@SIGMOD/PODS 2018: 10:1-10:5 - Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica:
Towards Fast and Scalable Graph Pattern Mining. HotCloud 2018 - Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica:
ASAP: Fast, Approximate Graph Pattern Mining at Scale. OSDI 2018: 745-761 - 2017
- 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) - 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) - 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) - Ashish Ranjan, Swagath Venkataramani, Zoha Pajouhi, Rangharajan Venkatesan, Kaushik Roy, Anand Raghunathan:
STAxCache: An approximate, energy efficient STT-MRAM cache. DATE 2017: 356-361 - Sanchari Sen, Swagath Venkataramani, Anand Raghunathan:
Approximate computing for spiking neural networks. DATE 2017: 193-198 - 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 - 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 - Sanjay Ganapathy, Swagath Venkataramani, Balaraman Ravindran, Anand Raghunathan:
DyVEDeep: Dynamic Variable Effort Deep Neural Networks. CoRR abs/1704.01137 (2017) - 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
- S. Kumaravel, Anand Kukde, B. Venkataramani, R. Raja:
A high linearity and high gain Folded Cascode LNA for narrowband receiver applications. Microelectron. J. 54: 101-108 (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): 3243-3256 (2016) - Swagath Venkataramani, Kaushik Roy, Anand Raghunathan:
Efficient embedded learning for IoT devices. ASP-DAC 2016: 308-311
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