Dataperf: Benchmarks for data-centric ai development
Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …
[HTML][HTML] Flood detection using real-time image segmentation from unmanned aerial vehicles on edge-computing platform
With the proliferation of unmanned aerial vehicles (UAVs) in different contexts and
application areas, efforts are being made to endow these devices with enough intelligence …
application areas, efforts are being made to endow these devices with enough intelligence …
Dynamic GPU energy optimization for machine learning training workloads
GPUs are widely used to accelerate the training of machine learning workloads. As modern
machine learning models become increasingly larger, they require a longer time to train …
machine learning models become increasingly larger, they require a longer time to train …
[HTML][HTML] A BenchCouncil view on benchmarking emerging and future computing
J Zhan - BenchCouncil Transactions on Benchmarks, Standards …, 2022 - Elsevier
The measurable properties of the artifacts or objects in the computer, management, or
finance disciplines are extrinsic, not inherent—dependent on their problem definitions and …
finance disciplines are extrinsic, not inherent—dependent on their problem definitions and …
Flbench: A benchmark suite for federated learning
Federated learning is a new machine learning paradigm. The goal is to build a machine
learning model from the data sets distributed on multiple devices–so-called an isolated data …
learning model from the data sets distributed on multiple devices–so-called an isolated data …
Scenario-based AI benchmark evaluation of distributed cloud/edge computing systems
Distributed cloud/edge (DCE) platform has become popular in recent years. This paper
proposes a new AI benchmark suite for assessing the performance of DCE platforms in …
proposes a new AI benchmark suite for assessing the performance of DCE platforms in …
[HTML][HTML] Understanding hot interconnects with an extensive benchmark survey
Understanding the designs and performance characterizations of hot interconnects on
modern data center and high-performance computing (HPC) clusters is a fruitful research …
modern data center and high-performance computing (HPC) clusters is a fruitful research …
Hpc ai500 v2. 0: The methodology, tools, and metrics for benchmarking hpc ai systems
Recent years witness a trend of applying large-scale distributed deep learning algorithms
(HPC AI) in both business and scientific computing areas, whose goal is to speed up the …
(HPC AI) in both business and scientific computing areas, whose goal is to speed up the …
Comprehensive complexity assessment of emerging learned image compression on cpu and gpu
F Pakdaman, M Gabbouj - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
Learned Compression (LC) is the emerging technology for compressing image and video
content, using deep neural networks. Despite being new, LC methods have already gained …
content, using deep neural networks. Despite being new, LC methods have already gained …
[HTML][HTML] Call for establishing benchmark science and engineering
J Zhan - BenchCouncil Transactions on Benchmarks, Standards …, 2021 - Elsevier
Currently, there is no consistent benchmarking across multi-disciplines. Even no previous
work tries to relate different categories of benchmarks in multi-disciplines. This article …
work tries to relate different categories of benchmarks in multi-disciplines. This article …