| Accelerating deep learning with dynamic data pruning RS Raju, K Daruwalla, M Lipasti arXiv preprint arXiv:2111.12621, 2021 | 12 | 2021 |
| BitSAD v2: Compiler Optimization and Analysis for Bitstream Computing K Daruwalla, H Zhuo, R Shukla, M Lipasti ACM Transactions on Architecture and Code Optimization (TACO) 16 (4), 1-25, 2019 | 8 | 2019 |
| Bitbench: A benchmark for bitstream computing K Daruwalla, H Zhuo, C Schulz, M Lipasti Proceedings of the 20th ACM SIGPLAN/SIGBED International Conference on …, 2019 | 7 | 2019 |
| BitSAD: A domain-specific language for bitstream computing K Daruwalla, H Zhuo Proceedings of the First ISCA Workshop on Unary Computing (WUC'19), 2019 | 3 | 2019 |
| Energy-Efficient Bayesian Inference Using Bitstream Computing S Khoram, K Daruwalla, M Lipasti IEEE Computer Architecture Letters, 2023 | 2 | 2023 |
| Building Energy Efficient Computers with Brain-Inspired Computing Models K Daruwalla The University of Wisconsin-Madison, 2022 | 1 | 2022 |
| Resource Efficient Navigation Using Bitstream Computing K Daruwalla, M Lipasti Proceedings of the First ISCA Workshop on Unary Computing (WUC'19), 2019 | 1 | 2019 |
| Information bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates K Daruwalla, M Lipasti Frontiers in Computational Neuroscience 18, 1240348, 2024 | | 2024 |
| A quantitative analysis of the performance of computing architectures used in neural simulations K Daruwalla, N Olivero, A Pluger, S Rao, DW Chang, M Simoni Journal of Neuroscience Methods 311, 57-66, 2019 | | 2019 |
Mikko LipastiProfessor, Electrical and Computer Engineering, University of Wisconsin-Madison在 engr.wisc.edu 的电子邮件经过验证
