1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning
Yeonsoo Jeon, Mattan Erez, Michael Orshansky
Privacy-Preserving ML (PPML) based on Homomorphic Encryption (HE) is a promising foundational privacy technology. Making it more practical requires lowering its computational cost,…
cs.LG2023
Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training
Zihao Deng, Benjamin Ghaemmaghami, Ashish Kumar Singh +4
Modern DNN-based recommendation systems rely on training-derived embeddings of sparse features. Input sparsity makes obtaining high-quality embeddings for rarely-occurring categori…
cs.AR2023★ 1 cited
Harvesting L2 Caches in Server Processors
Majid Jalili, Mattan Erez
We make three observations in modern processors: (1) LLC capacity is getting larger (up to 1GB); (2) core counts are increasing (up to 128 cores), accumulating a more significant a…