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ORCIDSujan K. Gonugondla
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2020 – today
- 2024
[c19]Haifeng Qian, Sujan Kumar Gonugondla, Sungsoo Ha, Mingyue Shang, Sanjay Krishna Gouda, Ramesh Nallapati, Sudipta Sengupta, Xiaofei Ma, Anoop Deoras:
BASS: Batched Attention-optimized Speculative Sampling. ACL (Findings) 2024: 8214-8224- [c18]Ben Athiwaratkun, Shiqi Wang, Mingyue Shang, Yuchen Tian, Zijian Wang, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Robert Kwiatkowski, Ramesh Nallapati, Parminder Bhatia, Bing Xiang:
Token Alignment via Character Matching for Subword Completion. ACL (Findings) 2024: 15725-15738 - [c17]Ben Athiwaratkun, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Haifeng Qian, Hantian Ding, Qing Sun, Jun Wang, Jiacheng Guo, Liangfu Chen, Parminder Bhatia, Ramesh Nallapati, Sudipta Sengupta, Bing Xiang:
Bifurcated Attention for Single-Context Large-Batch Sampling. ICML 2024 - [i7]Ben Athiwaratkun, Shiqi Wang, Mingyue Shang, Yuchen Tian, Zijian Wang, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Rob Kwiatkowski, Ramesh Nallapati, Bing Xiang:
Token Alignment via Character Matching for Subword Completion. CoRR abs/2403.08688 (2024) - [i6]Ben Athiwaratkun, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Haifeng Qian, Hantian Ding, Qing Sun, Jun Wang, Jiacheng Guo, Liangfu Chen, Parminder Bhatia, Ramesh Nallapati, Sudipta Sengupta, Bing Xiang:
Bifurcated Attention for Single-Context Large-Batch Sampling. CoRR abs/2403.08845 (2024) - [i5]Haifeng Qian, Sujan Kumar Gonugondla, Sungsoo Ha, Mingyue Shang, Sanjay Krishna Gouda, Ramesh Nallapati, Sudipta Sengupta, Xiaofei Ma, Anoop Deoras:
BASS: Batched Attention-optimized Speculative Sampling. CoRR abs/2404.15778 (2024) - 2023
- [c16]Wangxin He, Jian Meng, Sujan Kumar Gonugondla, Shimeng Yu, Naresh R. Shanbhag, Jae-sun Seo:
PRIVE: Efficient RRAM Programming with Chip Verification for RRAM-based In-Memory Computing Acceleration. DATE 2023: 1-6 - [c15]Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Kumar Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, Ramesh Nallapati:
Multi-lingual Evaluation of Code Generation Models. ICLR 2023 - [c14]Xiaokai Wei
, Sujan Kumar Gonugondla, Shiqi Wang, Wasi Uddin Ahmad, Baishakhi Ray, Haifeng Qian, Xiaopeng Li, Varun Kumar, Zijian Wang, Yuchen Tian, Qing Sun, Ben Athiwaratkun, Mingyue Shang, Murali Krishna Ramanathan, Parminder Bhatia, Bing Xiang:
Towards Greener Yet Powerful Code Generation via Quantization: An Empirical Study. ESEC/SIGSOFT FSE 2023: 224-236 - [i4]Xiaokai Wei, Sujan K. Gonugondla, Wasi Uddin Ahmad, Shiqi Wang, Baishakhi Ray, Haifeng Qian, Xiaopeng Li, Varun Kumar, Zijian Wang, Yuchen Tian, Qing Sun, Ben Athiwaratkun, Mingyue Shang, Murali Krishna Ramanathan, Parminder Bhatia, Bing Xiang:
Greener yet Powerful: Taming Large Code Generation Models with Quantization. CoRR abs/2303.05378 (2023) - 2022
- [j7]Sujan K. Gonugondla, Charbel Sakr, Hassan Dbouk, Naresh R. Shanbhag:
Fundamental Limits on Energy-Delay-Accuracy of In-Memory Architectures in Inference Applications. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 41(10): 3188-3201 (2022) - [c13]Sujan Kumar Gonugondla, Naresh R. Shanbhag:
IMPQ: Reduced Complexity Neural Networks Via Granular Precision Assignment. ICASSP 2022: 66-70 - [i3]Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Kumar Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang:
Multi-lingual Evaluation of Code Generation Models. CoRR abs/2210.14868 (2022) - 2021
- [j6]Hassan Dbouk, Sujan K. Gonugondla, Charbel Sakr, Naresh R. Shanbhag:
A 0.44-μJ/dec, 39.9-μs/dec, Recurrent Attention In-Memory Processor for Keyword Spotting. IEEE J. Solid State Circuits 56(7): 2234-2244 (2021) - 2020
- [b1]Sujan Kumar Gonugondla:
Cross-layer methods for energy-efficient inference using in-memory architectures. University of Illinois Urbana-Champaign, USA, 2020 - [j5]Mingu Kang, Sujan K. Gonugondla, Naresh R. Shanbhag:
Deep In-Memory Architectures in SRAM: An Analog Approach to Approximate Computing. Proc. IEEE 108(12): 2251-2275 (2020) - [c12]Chandrasekhar Radhakrishnan, Sujan K. Gonugondla:
Block-LMS and RLS adaptive filters using in-memory architectures. ACSSC 2020: 331-335 - [c11]Hassan Dbouk, Sujan K. Gonugondla, Charbel Sakr, Naresh R. Shanbhag:
KeyRAM: A 0.34 uJ/decision 18 k decisions/s Recurrent Attention In-memory Processor for Keyword Spotting. CICC 2020: 1-4 - [c10]Sujan K. Gonugondla, Ameya D. Patil, Naresh R. Shanbhag:
SWIPE: Enhancing Robustness of ReRAM Crossbars for In-memory Computing. ICCAD 2020: 93:1-93:9 - [c9]Sujan K. Gonugondla, Charbel Sakr, Hassan Dbouk, Naresh R. Shanbhag:
Fundamental Limits on the Precision of In-memory Architectures. ICCAD 2020: 128:1-128:9 - [i2]Sujan Kumar Gonugondla, Charbel Sakr, Hassan Dbouk, Naresh R. Shanbhag:
Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures in Inference Applications. CoRR abs/2012.13645 (2020)
2010 – 2019
- 2019
- [c8]Chandrasekhar Radhakrishnan, Sujan K. Gonugondla:
Adaptive Filtering in In-Memory-Based Architectures. ACSSC 2019: 784-788 - [c7]Ameya D. Patil, Haocheng Hua, Sujan K. Gonugondla, Mingu Kang, Naresh R. Shanbhag:
An MRAM-Based Deep In-Memory Architecture for Deep Neural Networks. ISCAS 2019: 1-5 - 2018
- [j4]Mingu Kang, Sungmin Lim, Sujan K. Gonugondla, Naresh R. Shanbhag:
An In-Memory VLSI Architecture for Convolutional Neural Networks. IEEE J. Emerg. Sel. Topics Circuits Syst. 8(3): 494-505 (2018) - [j3]Mingu Kang, Sujan K. Gonugondla, Ameya Patil, Naresh R. Shanbhag:
A Multi-Functional In-Memory Inference Processor Using a Standard 6T SRAM Array. IEEE J. Solid State Circuits 53(2): 642-655 (2018) - [j2]Mingu Kang, Sujan K. Gonugondla, Sungmin Lim, Naresh R. Shanbhag:
A 19.4-nJ/Decision, 364-K Decisions/s, In-Memory Random Forest Multi-Class Inference Accelerator. IEEE J. Solid State Circuits 53(7): 2126-2135 (2018) - [j1]Sujan K. Gonugondla, Mingu Kang, Naresh R. Shanbhag:
A Variation-Tolerant In-Memory Machine Learning Classifier via On-Chip Training. IEEE J. Solid State Circuits 53(11): 3163-3173 (2018) - [c6]Prakalp Srivastava, Mingu Kang, Sujan K. Gonugondla, Sungmin Lim, Jungwook Choi, Vikram S. Adve, Nam Sung Kim, Naresh R. Shanbhag:
PROMISE: An End-to-End Design of a Programmable Mixed-Signal Accelerator for Machine-Learning Algorithms. ISCA 2018: 43-56 - [c5]Sujan K. Gonugondla, Mingu Kang, Yongjune Kim, Mark Helm, Sean Eilert, Naresh R. Shanbhag:
Energy-Efficient Deep In-memory Architecture for NAND Flash Memories. ISCAS 2018: 1-5 - [c4]Sujan Kumar Gonugondla, Mingu Kang, Naresh R. Shanbhag:
A 42pJ/decision 3.12TOPS/W robust in-memory machine learning classifier with on-chip training. ISSCC 2018: 490-492 - 2017
- [c3]Mingu Kang, Sujan K. Gonugondla, Naresh R. Shanbhag:
A 19.4 nJ/decision 364K decisions/s in-memory random forest classifier in 6T SRAM array. ESSCIRC 2017: 263-266 - 2016
- [c2]Sujan K. Gonugondla, Byonghyo Shim, Naresh R. Shanbhag:
Perfect error compensation via algorithmic error cancellation. ICASSP 2016: 966-970 - [i1]Mingu Kang, Sujan K. Gonugondla, Ameya Patil, Naresh R. Shanbhag:
A 481pJ/decision 3.4M decision/s Multifunctional Deep In-memory Inference Processor using Standard 6T SRAM Array. CoRR abs/1610.07501 (2016) - 2015
- [c1]Mingu Kang, Sujan K. Gonugondla, Min-Sun Keel, Naresh R. Shanbhag:
An energy-efficient memory-based high-throughput VLSI architecture for convolutional networks. ICASSP 2015: 1037-1041
Coauthor Index
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last updated on 2024-09-26 01:50 CEST by the dblp team
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