38 citations · 57 across the 6 of their papers we have counts for
8 papers
DisaggRec: Architecting Disaggregated Systems for Large-Scale Personalized Recommendation
Liu Ke, Xuan Zhang, Benjamin Lee +2
Deep learning-based personalized recommendation systems are widely used for online user-facing services in production datacenters, where a large amount of hardware resources are pr…
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…
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance
Udit Gupta, Samuel Hsia, Jeff Zhang +6
Deep learning recommendation systems must provide high quality, personalized content under strict tail-latency targets and high system loads. This paper presents RecPipe, a system…
Chasing Carbon: The Elusive Environmental Footprint of Computing
Udit Gupta, Young Geun Kim, Sylvia Lee +5
Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its…
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference
Brandon Reagen, Wooseok Choi, Yeongil Ko +4
As the application of deep learning continues to grow, so does the amount of data used to make predictions. While traditionally, big-data deep learning was constrained by computing…
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
Udit Gupta, Samuel Hsia, Vikram Saraph +6
Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure. Thu…