Projects per year
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Collaborations and top research areas from the last five years
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CNS Core: Small: CNN-Accelerator Co-Design
NSF - National Science Foundation
8/1/22 → 7/31/25
Project: Research project
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CCF: SHF: Small: Transformer synthesis
NSF - National Science Foundation
2/1/22 → 1/31/25
Project: Research project
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CNS Core: Small: Ultra-Efficient Neural Network and LSTM Architectures
NSF - National Science Foundation
10/1/19 → 9/30/23
Project: Research project
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SHF: Small: Extremely Energy-Efficient Monolithic 3D System Architectures
NSF - National Science Foundation
7/1/18 → 6/30/22
Project: Research project
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SHF: Small: Exploration of the Transistor-level Monolithic 3D SRAM Design Space
NSF - National Science Foundation
9/1/17 → 8/31/21
Project: Research project
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AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with Transformers
Tuli, S. & Jha, N. K., 2023, (Accepted/In press) In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. p. 1 1 p.Research output: Contribution to journal › Article › peer-review
1 Scopus citations -
AIMED: AI-Mediated Exploration of Design: An Experience Report
Narain, S., Chee, D., Iyer, P., Mak, E., Valdez, R., Zhu, M., Jha, N., Fisac, J., Hsu, K. C., Terway, P., Pochiraju, K., Englot, B., Pitz, E., Rooney, S. & Huang, Y., May 9 2023, Proceedings of 2023 Cyber-Physical Systems and Internet-of-Things Week, CPS-IoT Week 2023 - Workshops. Association for Computing Machinery, p. 136-140 5 p. (ACM International Conference Proceeding Series).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework
Tuli, S., Li, C. H., Sharma, R. & Jha, N. K., Apr 20 2023, In: ACM Transactions on Embedded Computing Systems. 22, 3, 51.Research output: Contribution to journal › Article › peer-review
Open Access -
FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?
Tuli, S., Dedhia, B., Tuli, S. & Jha, N. K., 2023, In: Journal of Artificial Intelligence Research. 77, p. 39-70 32 p.Research output: Contribution to journal › Article › peer-review
Open Access -
How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics
Li, H., Serrano, D., Guillod, T., Wang, S., Dogariu, E., Nadler, A., Luo, M., Bansal, V., Jha, N. K., Chen, Y., Sullivan, C. R. & Chen, M., 2023, (Accepted/In press) In: IEEE Transactions on Power Electronics. p. 1-25 25 p.Research output: Contribution to journal › Article › peer-review
Open Access