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Nandan Kumar Jha
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2020 – today
- 2024
- [j3]Nandan Kumar Jha, Brandon Reagen:
DeepReShape: Redesigning Neural Networks for Efficient Private Inference. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [c8]Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, Brandon Reagen:
Characterizing and Optimizing End-to-End Systems for Private Inference. ASPLOS (3) 2023: 89-104 - [i13]Nandan Kumar Jha, Brandon Reagen:
DeepReShape: Redesigning Neural Networks for Efficient Private Inference. CoRR abs/2304.10593 (2023) - 2022
- [i12]Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, Brandon Reagen:
Characterizing and Optimizing End-to-End Systems for Private Inference. CoRR abs/2207.07177 (2022) - 2021
- [j2]Nandan Kumar Jha
, Sparsh Mittal
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance. IEEE Trans. Computers 70(9): 1526-1538 (2021) - [c7]Chika Udeaja
Digital Storytelling: The Integration of Intangible and Tangible Heritage in the City of Surat, India. HCI (33) 2021: 152-168 - [c6]Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen:
DeepReDuce: ReLU Reduction for Fast Private Inference. ICML 2021: 4839-4849 - [c5]Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg:
Circa: Stochastic ReLUs for Private Deep Learning. NeurIPS 2021: 2241-2252 - [i11]Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen:
DeepReDuce: ReLU Reduction for Fast Private Inference. CoRR abs/2103.01396 (2021) - [i10]Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg:
Circa: Stochastic ReLUs for Private Deep Learning. CoRR abs/2106.08475 (2021) - [i9]Karthik Garimella, Nandan Kumar Jha, Brandon Reagen:
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning. CoRR abs/2107.12342 (2021) - [i8]Karthik Garimella, Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen:
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale. CoRR abs/2111.02583 (2021) - 2020
- [j1]Nandan Kumar Jha, Sparsh Mittal, Binod Kumar, Govardhan Mattela:
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs. ACM J. Emerg. Technol. Comput. Syst. 17(1): 5:1-5:25 (2020) - [c4]Nandan Kumar Jha, Shreyas Ravishankar, Sparsh Mittal, Arvind Kaushik, Dipan Mandal, Mahesh Chandra
DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator. ISVLSI 2020: 574-579 - [c3]Nandan Kumar Jha, Rajat Saini, Subhrajit Nag, Sparsh Mittal:
E2GC: Energy-efficient Group Convolution in Deep Neural Networks. VLSID 2020: 155-160 - [c2]Rajat Saini, Nandan Kumar Jha, Bedanta Das, Sparsh Mittal, C. Krishna Mohan
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks. WACV 2020: 1616-1625 - [i7]Nandan Kumar Jha, Sparsh Mittal, Govardhan Mattela:
The Ramifications of Making Deep Neural Networks Compact. CoRR abs/2006.15098 (2020) - [i6]Nandan Kumar Jha, Rajat Saini, Subhrajit Nag, Sparsh Mittal:
E2GC: Energy-efficient Group Convolution in Deep Neural Networks. CoRR abs/2006.15100 (2020) - [i5]Rajat Saini, Nandan Kumar Jha, Bedanta Das, Sparsh Mittal, C. Krishna Mohan:
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks. CoRR abs/2006.15102 (2020) - [i4]Nandan Kumar Jha, Shreyas Ravishankar, Sparsh Mittal, Arvind Kaushik, Dipan Mandal, Mahesh Chandra:
DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator. CoRR abs/2006.15103 (2020) - [i3]Nandan Kumar Jha, Rajat Saini, Sparsh Mittal:
On the Demystification of Knowledge Distillation: A Residual Network Perspective. CoRR abs/2006.16589 (2020) - [i2]Nandan Kumar Jha, Sparsh Mittal, Binod Kumar, Govardhan Mattela:
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs. CoRR abs/2007.15248 (2020) - [i1]Nandan Kumar Jha, Sparsh Mittal:
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance. CoRR abs/2008.02565 (2020)
2010 – 2019
- 2019
- [c1]Nandan Kumar Jha, Sparsh Mittal
The Ramifications of Making Deep Neural Networks Compact. VLSID 2019: 215-220
Coauthor Index
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