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What is EdgeCortix?

Advancing AI processors and expediting edge AI inference has become vital in the modern technological environment. In contexts where swift AI inference is critical, the need for higher TOPS, lower latency, improved area and power efficiency, and scalability takes precedence, and EdgeCortix AI processor cores meet these requirements effectively. Although general-purpose processing units, such as CPUs and GPUs, provide some flexibility across various applications, they frequently struggle to fulfill the unique needs of deep neural network tasks. EdgeCortix was established with a mission to revolutionize edge AI processing fundamentally. By providing a robust AI inference software development platform, customizable edge AI inference IP, and specialized edge AI chips for hardware integration, EdgeCortix enables designers to realize cloud-level AI performance directly at the edge of networks. This progress not only enhances existing technologies but also opens up new avenues for innovation in areas like threat detection, improved situational awareness, and the development of smarter vehicles, which contribute to creating safer and more intelligent environments. The ripple effect of these advancements could redefine how industries operate, leading to unprecedented levels of efficiency and safety across various sectors.

What is AWS Inferentia?

AWS has introduced Inferentia accelerators to enhance performance and reduce expenses associated with deep learning inference tasks. The original version of this accelerator is compatible with Amazon Elastic Compute Cloud (Amazon EC2) Inf1 instances, delivering throughput gains of up to 2.3 times while cutting inference costs by as much as 70% in comparison to similar GPU-based EC2 instances. Numerous companies, including Airbnb, Snap, Sprinklr, Money Forward, and Amazon Alexa, have successfully implemented Inf1 instances, reaping substantial benefits in both efficiency and affordability. Each first-generation Inferentia accelerator comes with 8 GB of DDR4 memory and a significant amount of on-chip memory. In comparison, Inferentia2 enhances the specifications with a remarkable 32 GB of HBM2e memory per accelerator, providing a fourfold increase in overall memory capacity and a tenfold boost in memory bandwidth compared to the first generation. This leap in technology places Inferentia2 as an optimal choice for even the most resource-intensive deep learning tasks. With such advancements, organizations can expect to tackle complex models more efficiently and at a lower cost.

Media

Media

Integrations Supported

Docker
Jupyter Notebook
PyTorch
TensorFlow
Ubuntu

Integrations Supported

AWS EC2 Trn3 Instances
AWS Parallel Computing Service
Amazon EC2 Inf1 Instances
Amazon EC2 Trn1 Instances
Anyscale
WithoutBG

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

EdgeCortix

Company Location

Japan

Company Website

www.edgecortix.com/en/

Company Facts

Organization Name

Amazon

Date Founded

2006

Company Location

United States

Company Website

aws.amazon.com/machine-learning/inferentia/

Categories and Features

AI Inference

Not specified

Categories and Features

AI Inference

Not specified

AI Infrastructure

Not specified

Deep Learning

Not specified

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