Edge Impulse Integrates Microchip’s SAMA7G54 Microprocessor Into Its Platform to Easily Train Machine Learning Models

Edge Impulse, the leading platform for building, deploying, and scaling edge machine learning models, today announces Microchip Technology’s SAMA7G54 microprocessor (MPU) is now fully integrated int...

Autore: Business Wire

SAN JOSE, Calif.: Edge Impulse, the leading platform for building, deploying, and scaling edge machine learning models, today announces Microchip Technology’s SAMA7G54 microprocessor (MPU) is now fully integrated into the Edge Impulse platform. This collaboration marks a significant milestone in advancing edge machine learning capabilities, particularly in camera-based applications.

The integration allows developers to build, train and deploy cutting-edge machine learning models directly on edge devices powered by the SAMA7G54. This development is set to accelerate the adoption of AI at the edge, offering unprecedented opportunities in various industries, from smart home devices to industrial automation.

“The Edge Impulse platform is designed to provide engineers with an easy-to-use device to develop AI/ML models,” said Zach Shelby, co-founder and CEO at Edge Impulse. “Our collaboration with Microchip to integrate the SAMA7G54 MPU into the platform aligns with our vision to empower developers to bring more AI products to market in weeks instead of months or years.”

“This innovative solution leverages Microchip’s high-performance single-core MPU and the Edge Impulse platform to enable developers to train, evaluate and deploy machine learning (ML) models,” said Rod Drake, corporate vice president of Microchip’s MPU32 and MCU32 business units. “The adoption of AI at the edge is rapidly increasing across many applications and this solution is designed to significantly streamline the process from the design phase through implementation.”

Microchip’s SAMA7G54 is a high-performance single-core microprocessor that boasts several impressive features for edge AI applications. This powerful MPU supports camera-based edge machine learning tools like Edge Impulse’s Faster Objects, More Objects (FOMO) algorithm, which brings object detection to highly constrained devices, and image classification models, expanding the horizons for developers working on innovative AI solutions.

Further, the high efficiency SAMA7G54 is optimized for low power consumption without compromising performance and is equipped with an Arm® Cortex®-A7 core. The device also features enhanced connectivity with dual Ethernet options, a built-in camera using a 12-bit parallel or MIPI-CSI2 and audio subsystem, as well as advanced security such as secure boot and hardware cryptography accelerators.

For more information on integrating the Microchip SAMA7G54 with Edge Impulse, please visit the Edge Impulse documentation here and Microchip’s documentation here.

About Edge Impulse

Edge Impulse streamlines the creation of AI and machine learning models for edge hardware, allowing devices to make decisions and offer insight where data is gathered. Edge Impulse’s technology empowers developers to bring more AI products to market, and helps enterprise teams rapidly develop production-ready solutions in weeks instead of years. Powerful automations make it easier to build valuable datasets and develop advanced AI for edge devices from MCUs to CPUs to GPUs. Used by health and wearable organizations like Know Labs and Neurable, industrial organizations like TKE and Lexmark, as well as top silicon vendors and over 100,000 developers, Edge Impulse has become the trusted ML platform for enterprises and developers alike. To learn more, visit edgeimpulse.com.

Fonte: Business Wire


Visualizza la versione completa sul sito

Informativa
Questo sito o gli strumenti terzi da questo utilizzati si avvalgono di cookie necessari al funzionamento ed utili alle finalità illustrate nella cookie policy. Se vuoi saperne di più o negare il consenso a tutti o ad alcuni cookie, consulta la cookie policy. Chiudendo questo banner, acconsenti all’uso dei cookie.