9 citations · 21 across the 14 of their papers we have counts for
4 papers · 1 filter
MaxMem: Colocation and Performance for Big Data Applications on Tiered Main Memory Servers
Amanda Raybuck, Wei Zhang, Kayvan Mansoorshahi +3
We present MaxMem, a tiered main memory management system that aims to maximize Big Data application colocation and performance. MaxMem uses an application-agnostic and lightweight…
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,…
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…
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…