Driving the Evolution of Autonomous Driving Technology
Deliver exceptional performance and scalability to accelerate the training of deep neural network algorithms, enabling more accurate predictions and a smoother autonomous driving experience.
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Legacy Infrastructure Constraints Impeding Autonomous Driving Technology
Massive Amounts of Road Data
Autonomous vehicles rely on a network of sensors, including cameras, GPS, sonars, lidars, and radars, to navigate diverse environments. These sensors generate up to 3-10 TB of road data per day.
Challenges of Flexible Data Flow
Test data is siloed in self-driving enterprises' hybrid cloud IT systems. Traditional copy methods in data preprocessing, training, simulation, and result analysis result in lengthy wait times at each stage, severely hindering the iteration of autonomous driving models.
Need for High IOPS & Low Latency
In autonomous driving model training, datasets often comprise massive small files that are read repeatedly by GPUs. It's critical to overcome performance challenges for high IOPS and low latency to ensure that GPUs can quickly access and process these small files.
Accelerate Autonomous Driving Technology to Modern AI Speeds with YRCloudFile
YanRong has a deep understanding of the challenges in managing data across the entire autonomous driving workflow. YRCloudFile and all-flash F9000X/F8000X are specially designed to make AI-accelerated data workflows simple to deploy and manage on-premises and in the hybrid cloud.
YRCloudFile is designed to meet the demanding requirements of your self-driving pipelines, offering high performance and scalability to handle all your road data. Leveraging YRCloudFile’s distributed architecture, data and metadata nodes can be scaled on demand to accommodate massive daily data growth.
For model training, YRCloudFile is optimized for fast, low-latency, and high-throughput I/O access, accelerating the iteration of your autonomous driving models. We help you achieve unprecedented scalability and performance, driving your self-driving technology forward with confidence.
Advantages
Superior Performance
YRCloudFile delivers exceptional performance across all IO patterns, handling users' demanding autonomous driving workloads, from large datasets with sequential IO operations to numerous small files with random IO patterns.
A Unified & Multi-Protocol Platform
Supports multiple protocols to build a unified data space, eliminating data silos and enabling applications to seamlessly store, access, and analyze massive unstructured data across cloud, edge, and core.
Flexible Data Movement
The DataLoad feature bridges file storage with object storage, allowing data to flow flexibly and on-demand between on-premises and multi-cloud environments.
Scalability
Utilizes a scalable MDS architecture that enables data expansion alongside business growth. Supports handling billions of files in autonomous driving.
Reduce Storage Costs
YRCloudFile offers fully automated deployment, saving time and effort. It features graphical operation and management, making it both simple and efficient.