77 citations · 148 across the 5 of their papers we have counts for
5 papers
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
Mark Zhao, Dhruv Choudhary, Devashish Tyagi +9
We present RecD (Recommendation Deduplication), a suite of end-to-end infrastructure optimizations across the Deep Learning Recommendation Model (DLRM) training pipeline. RecD addr…
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…
ShEF: Shielded Enclaves for Cloud FPGAs
Mark Zhao, Mingyu Gao, Christos Kozyrakis
FPGAs are now used in public clouds to accelerate a wide range of applications, including many that operate on sensitive data such as financial and medical records. We present ShEF…
Llama: A Heterogeneous & Serverless Framework for Auto-Tuning Video Analytics Pipelines
Francisco Romero, Mark Zhao, Neeraja J. Yadwadkar +1
The proliferation of camera-enabled devices and large video repositories has led to a diverse set of video analytics applications. These applications rely on video pipelines, repre…
To Centralize or Not to Centralize: A Tale of Swarm Coordination
Justin Hu, Ariana Bruno, Drew Zagieboylo +7
Large swarms of autonomous devices are increasing in size and importance. When it comes to controlling the devices of large-scale swarms there are two main lines of thought. Centra…