Deploying AI Models at the Edge

Alif Semiconductor, Telit bring machine learning, wireless connectivity capabilities to edge IoT with developer kit

Categories Connectivity  |  Edge Computing News  |  Hardware
Alif Semiconductor, Telit bring machine learning, wireless connectivity capabilities to edge IoT with developer kit

Alif Semiconductor, a provider of low-power AI fusion processors and microcontrollers for IoT applications, has partnered with Telit, a cloud connectivity IoT solutions provider. The co-developed developers kit combines each other’s strengths and expertise. It brings wireless cloud connectivity solutions and machine learning capabilities to distributed and IoT edge environments. The developer kit provides system engineers with cloud-connected hardware and software reference design, allowing them to focus on AI-powered computer vision, voice and sensor applications. 

The developer kit leverages the Alif Ensemble family of microcontrollers and fusion processors and Telit’s wireless connectivity module. The collaboration utilizes Telit’s cloud services and platforms to manage and deploy IoT edge devices effectively. The developer kit allows users to speed up their design process and prototype IoT edge applications that can sense data and make quick decisions.

“Together, Alif’s scalable low-power secure processors plus Telit’s communication modules and cloud services make an ideal combination for anyone who wants to simply and rapidly develop and deploy IoT edge devices using the very latest technologies,” said Reza Kazerounian, co-founder and president at Alif Semiconductor. “The barriers are now removed to bring low-power Machine Learning to the edge, worldwide.”

The Alif Ensemble microcontrollers and fusion processors are designed for high-performance embedded processing, featuring single to quad-core processors, AI accelerators and high-speed connectivity. The modules for this system vary from single-core Arm Cortex-M55 microcontrollers to multi-core devices including two Arm Cortex MCU cores and up to two additional Arm cortex A32 processor cores. They also feature Arm Ethos-U55 microNPUs to accelerate machine learning.

Telif wireless connectivity modules range from cellular networks for IoT and M2M applications to Wi-Fi and Bluetooth modules for low-power IoT devices. The developer kit includes a Telit module that supports Wi-Fi, Bluetooth, LTE and 5G cellular, including Cat-M and NB-IoT. Telit offers 5G modules with sub-6 and mmWave technologies to support a wide range of industries and applications, including high-power fixed wireless access, enterprise router and gateways, broadcasting and surveillance.

“This collaboration delivers comprehensive IoT enablement for solutions leveraging the power of Alif processors for the vibrant new space of low-power and battery-powered AI/ML edge devices,” said Manish Watwani, chief marketing and product officer at Telit. 

Alif Semiconductor, Bosch Sensortec, and Edge Impulse join forces to simplify machine vision applications

Alif Semiconductor partnered with Bosch Sensortech and Edge Impulse to reduce the complexity involved with developing precision motion sensing products. The collaboration seeks to create a high-performing, power-efficient, highly secure solution for edge IoT products. For this initiative, they integrated Alif’s Ensemble family low-power E3 MCU with Bosch Sensortech BMI323 IMU and the Edge Impulse development platform for embedded machine learning.

The company claims that its results outperform traditional methodologies. According to them, an ML model can be created, trained and deployed on the E3 microcontroller in less than one hour. This solution can quickly identify complex motion patterns from multiple directions. Once it identifies the gesture pattern, it only takes 280 milliseconds to confirm.

“One of the key drivers behind the architecture of the Ensemble family has been to deliver a higher degree of integrated high-level functions to edge platforms than ever seen before,” Kazerounian added. “Being able to handle this performance level of machine learning workloads within the power and price budget of an MCU is unmatched in the industry today.”

Article Topics

 |   |   |   |   |   |   |   |   | 

Comments

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Featured Edge Computing Company

Edge Ecosystem Videos

Deploying AI Models at the Edge

“Deploying

Latest News