6 papers
Communication-Efficient and Drift-Robust Federated Learning via Elastic Net
Seonhyeong Kim, Jiheon Woo, Daewon Seo +1
Federated learning (FL) is a distributed method to train a global model over a set of local clients while keeping data localized. It reduces the risks of privacy and security but f…
On the Efficient Estimation of Min-Entropy
Yongjune Kim, Cyril Guyot, Young-Sik Kim
The min-entropy is a widely used metric to quantify the randomness of generated random numbers in cryptographic applications; it measures the difficulty of guessing the most likely…
Optimizing the Write Fidelity of MRAMs
Yongjune Kim, Yoocharn Jeon, Cyril Guyot +1
Magnetic random-access memory (MRAM) is a promising memory technology due to its high density, non-volatility, and high endurance. However, achieving high memory fidelity incurs si…
Boosting Classifiers with Noisy Inference
Yongjune Kim, Yuval Cassuto, Lav R. Varshney
We present a principled framework to address resource allocation for realizing boosting algorithms on substrates with communication or computation noise. Boosting classifiers (e.g.…
On the Optimal Refresh Power Allocation for Energy-Efficient Memories
Yongjune Kim, Won Ho Choi, Cyril Guyot +1
Refresh is an important operation to prevent loss of data in dynamic random-access memory (DRAM). However, frequent refresh operations incur considerable power consumption and degr…
Redundancy allocation in finite-length nested codes for nonvolatile memories
Yongjune Kim, B. V. K. Vijaya Kumar
In this paper, we investigate the optimum way to allocate redundancy of finite-length nested codes for modern nonvolatile memories suffering from both permanent defects and transie…