7 citations · 9 across the 3 of their papers we have counts for
4 papers
Hercules: Heterogeneity-Aware Inference Serving for At-Scale Personalized Recommendation
Liu Ke, Udit Gupta, Mark Hempstead +3
Personalized recommendation is an important class of deep-learning applications that powers a large collection of internet services and consumes a considerable amount of datacenter…
NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories
Lillian Pentecost, Alexander Hankin, Marco Donato +3
Repeated off-chip memory accesses to DRAM drive up operating power for data-intensive applications, and SRAM technology scaling and leakage power limits the efficiency of embedded…
Understanding Capacity-Driven Scale-Out Neural Recommendation Inference
Michael Lui, Yavuz Yetim, Özgür Özkan +4
Deep learning recommendation models have grown to the terabyte scale. Traditional serving schemes--that load entire models to a single server--are unable to support this scale. One…
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
Liu Ke, Udit Gupta, Carole-Jean Wu +18
Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embed…