82 citations · 167 across the 13 of their papers we have counts for
5 papers · 1 filter
TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments
Abdul Dakkak, Cheng Li, Simon Garcia de Gonzalo +2
Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detect…
Frustrated with Replicating Claims of a Shared Model? A Solution
Abdul Dakkak, Cheng Li, Jinjun Xiong +1
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that model owners and evaluators are hard-pressed analyzing and studying them. Th…
Accelerating Reduction and Scan Using Tensor Core Units
Abdul Dakkak, Cheng Li, Isaac Gelado +2
Driven by deep learning, there has been a surge of specialized processors for matrix multiplication, referred to as TensorCore Units (TCUs). These TCUs are capable of performing ma…
SCOPE: C3SR Systems Characterization and Benchmarking Framework
Carl Pearson, Abdul Dakkak, Cheng Li +3
This report presents the design of the Scope infrastructure for extensible and portable benchmarking. Improvements in high- performance computing systems rely on coordination acros…
Matrix Factorization on GPUs with Memory Optimization and Approximate Computing
Wei Tan, Shiyu Chang, Liana Fong +3
Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative filtering, data compression, feature extraction…