82 citations · 159 across the 11 of their papers we have counts for
11 papers
DLSpec: A Deep Learning Task Exchange Specification
Abdul Dakkak, Cheng Li, Jinjun Xiong +1
Deep Learning (DL) innovations are being introduced at a rapid pace. However, the current lack of standard specification of DL tasks makes sharing, running, reproducing, and compar…
MLModelScope: A Distributed Platform for Model Evaluation and Benchmarking at Scale
Abdul Dakkak, Cheng Li, Jinjun Xiong +1
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them. The complicated proc…
The Design and Implementation of a Scalable DL Benchmarking Platform
Cheng Li, Abdul Dakkak, Jinjun Xiong +1
The current Deep Learning (DL) landscape is fast-paced and is rife with non-uniform models, hardware/software (HW/SW) stacks, but lacks a DL benchmarking platform to facilitate eva…
DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)
Cheng Li, Abdul Dakkak, Jinjun Xiong +1
The past few years have seen a surge of applying Deep Learning (DL) models for a wide array of tasks such as image classification, object detection, machine translation, etc. While…
Benanza: Automatic Benchmark Generation to Compute "Lower-bound" Latency and Inform Optimizations of Deep Learning Models on GPUs
Cheng Li, Abdul Dakkak, Jinjun Xiong +1
As Deep Learning (DL) models have been increasingly used in latency-sensitive applications, there has been a growing interest in improving their response time. An important venue f…
XSP: Across-Stack Profiling and Analysis of Machine Learning Models on GPUs
Cheng Li, Abdul Dakkak, Jinjun Xiong +3
There has been a rapid proliferation of machine learning/deep learning (ML) models and wide adoption of them in many application domains. This has made profiling and characterizati…
Challenges and Pitfalls of Machine Learning Evaluation and Benchmarking
Cheng Li, Abdul Dakkak, Jinjun Xiong +1
An increasingly complex and diverse collection of Machine Learning (ML) models as well as hardware/software stacks, collectively referred to as "ML artifacts", are being proposed -…
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…