394 citations · 696 across the 22 of their papers we have counts for
22 papers
Socio-Technological Challenges and Opportunities: Paths Forward
Carole-Jean Wu, Srilatha Manne, Parthasarathy Ranganathan +2
Advancements in digital technologies have a bootstrapping effect. The past fifty years of technological innovations from the computer architecture community have brought innovation…
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
Young Geun Kim, Carole-Jean Wu
Federated learning enables a cluster of decentralized mobile devices at the edge to collaboratively train a shared machine learning model, while keeping all the raw training sample…
SVP-CF: Selection via Proxy for Collaborative Filtering Data
Noveen Sachdeva, Carole-Jean Wu, Julian McAuley
We study the practical consequences of dataset sampling strategies on the performance of recommendation algorithms. Recommender systems are generally trained and evaluated on sampl…
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale
Zhaoxia, Deng, Jongsoo Park +17
Tremendous success of machine learning (ML) and the unabated growth in ML model complexity motivated many ML-specific designs in both CPU and accelerator architectures to speed up…
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…
RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference
Mark Wilkening, Udit Gupta, Samuel Hsia +4
Neural personalized recommendation models are used across a wide variety of datacenter applications including search, social media, and entertainment. State-of-the-art models compr…