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Average Ratings 0 Ratings
Description
AWS Inferentia accelerators, engineered by AWS, aim to provide exceptional performance while minimizing costs for deep learning (DL) inference tasks. The initial generation of AWS Inferentia accelerators supports Amazon Elastic Compute Cloud (Amazon EC2) Inf1 instances, boasting up to 2.3 times greater throughput and a 70% reduction in cost per inference compared to similar GPU-based Amazon EC2 instances. Numerous companies, such as Airbnb, Snap, Sprinklr, Money Forward, and Amazon Alexa, have embraced Inf1 instances and experienced significant advantages in both performance and cost. Each first-generation Inferentia accelerator is equipped with 8 GB of DDR4 memory along with a substantial amount of on-chip memory. The subsequent Inferentia2 model enhances capabilities by providing 32 GB of HBM2e memory per accelerator, quadrupling the total memory and decoupling the memory bandwidth, which is ten times greater than its predecessor. This evolution in technology not only optimizes the processing power but also significantly improves the efficiency of deep learning applications across various sectors.
Description
The µ-velOSity RTOS stands out as the most compact option within Green Hills Software's suite of real-time operating systems. Developed as a C library, it is highly adaptable for various target architectures, facilitating easy integration. Its streamlined architecture is closely aligned with the MULTI IDE, making µ-velOSity not only straightforward to learn but also user-friendly. By providing a clear and concise API, it helps to shorten development timelines and enhance the maintainability of products. Consequently, this can lead to cost reductions and faster time-to-market for developers transitioning from standalone or no-OS setups. Thanks to its efficient architecture and small memory footprint, µ-velOSity outperforms many competitors by fitting seamlessly within on-chip memory. This design choice eliminates reliance on off-chip memory, significantly boosting execution speed. Furthermore, the RTOS has been engineered to minimize CPU clock cycles during booting, an essential feature for embedded systems that demand rapid startup times. Additionally, µ-velOSity is exceptionally suited for embedded devices that have strict power consumption constraints, ensuring optimal performance without compromising energy efficiency. In summary, µ-velOSity provides a robust solution for developers seeking a reliable and efficient RTOS for various embedded applications.
API Access
Has API
API Access
Has API
Integrations
AWS EC2 Trn3 Instances
AWS Parallel Computing Service
Amazon EC2 Inf1 Instances
Amazon EC2 Trn1 Instances
Anyscale
MULTI IDE
TimeMachine
WithoutBG
Integrations
AWS EC2 Trn3 Instances
AWS Parallel Computing Service
Amazon EC2 Inf1 Instances
Amazon EC2 Trn1 Instances
Anyscale
MULTI IDE
TimeMachine
WithoutBG
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Amazon
Founded
2006
Country
United States
Website
aws.amazon.com/machine-learning/inferentia/
Vendor Details
Company Name
Green Hills Software
Founded
1982
Country
United States
Website
www.ghs.com/products/micro_velosity.html
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Infrastructure-as-a-Service (IaaS)
Analytics / Reporting
Configuration Management
Data Migration
Data Security
Load Balancing
Log Access
Network Monitoring
Performance Monitoring
SLA Monitoring