394 citations · 1.4k across the 41 of their papers we have counts for
9 papers · 1 filter
GreenScale: Carbon-Aware Systems for Edge Computing
Young Geun Kim, Udit Gupta, Andrew McCrabb +4
To improve the environmental implications of the growing demand of computing, future applications need to improve the carbon-efficiency of computing infrastructures. State-of-the-a…
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
Haiyang Huang, Newsha Ardalani, Anna Sun +6
Mixture-of-Experts (MoE) models have gained popularity in achieving state-of-the-art performance in a wide range of tasks in computer vision and natural language processing. They e…
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…
Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka +5
Technology companies have been leading the way to a renewable energy transformation, by investing in renewable energy sources to reduce the carbon footprint of their datacenters. I…
Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training
Mark Zhao, Niket Agarwal, Aarti Basant +14
Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators (DSA) are used to train increasingly-complex deep learning models. These clusters rely…
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…
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…
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…
The Architectural Implications of Facebook's DNN-based Personalized Recommendation
Udit Gupta, Carole-Jean Wu, Xiaodong Wang +12
The widespread application of deep learning has changed the landscape of computation in the data center. In particular, personalized recommendation for content ranking is now large…