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ease
features
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support

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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 No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

AWS EC2 Trn3 Instances Yes 
AWS Parallel Computing Service Yes 
Amazon EC2 Inf1 Instances Yes 
Amazon EC2 Trn1 Instances Yes 
Anyscale Yes 
MULTI IDE No 
TimeMachine No 
WithoutBG Yes 

Integrations

AWS EC2 Trn3 Instances No 
AWS Parallel Computing Service No 
Amazon EC2 Inf1 Instances No 
Amazon EC2 Trn1 Instances No 
Anyscale No 
MULTI IDE Yes 
TimeMachine Yes 
WithoutBG No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person Yes 

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 No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

Infrastructure-as-a-Service (IaaS)

Analytics / Reporting No 
Configuration Management No 
Data Migration No 
Data Security No 
Load Balancing No 
Log Access No 
Network Monitoring No 
Performance Monitoring No 
SLA Monitoring No 

Alternatives

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