5 citations · 7 across the 3 of their papers we have counts for
6 papers · 1 filter
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…
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 -…
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…