Ambarella and eInfochips Provide Design Services for AI Edge Vision Devices
Ambarella (Santa Clara, CA, USA; www.ambarella.com), a vendor of edge AI semiconductor products, and eInfochips (San Jose, CA, USA; www.einfochips.com), a provider of engineering services, have forged a partnership to provide design expertise to developers of AI-based cameras that incorporate Ambarella’s CVflow edge AI SoC processors.
“This synergistic collaboration will unleash our deep knowledge and design expertise for companies looking to take full advantage of the excellent AI performance per watt that Ambarella can bring to their computer vision designs,” Parag Mehta, Chief Sales and Business Development Officer for eInfochips, says in a news release announcing the partnership.
eInfochips’ design expertise spans a variety of camera technologies and protocols, including RGB-IR cameras, PTZ, ONVIF, PSIA, ZigBee wireless, on-camera DVRs, and multi-imager designs.
Ambarella’s CVflow chip technology “includes a dedicated vision processing engine programmed with a high-level algorithm description, allowing the architecture to perform trillions of operations per second with low power consumption,” the company says.
The relationship between the two companies leverages eInfochips’ engineering experience and resources to support the rapid growth of AI-based vision IoT applications in robot perception, video conferencing, and security, among others. “One example is automated mobile robots for warehouses, where the robot is able to navigate the warehouse to pick products for order shipments while avoiding people, forklifts, and other obstacles. Another is access control devices that are able to use a combination of cameras and 3D sensing to provide facial recognition for entry into secure locations while preventing access via the use of photos or ‘tailgating’ to enter immediately behind someone who has been authorized,” Chris Day, Vice President of Marketing and Business Development for Ambarella, explains.
There is a substantial market for AI edge applications in a variety of commercial applications. “Globally, there was an installed base of about 8.4 billion IoT end point devices at the end of 2021, with deep-learning processors in the early stage of penetrating this rapidly growing market,” Lian Jye Su, Director of AI Research at ABI Research, says. “ABI projects over 12% penetration of deep learning into the AI IoT end point market by 2028,” he adds.
The growth in IoT includes devices incorporating computer vision, including those applicable to the next generation of machine vision applications. In its own news release written a few years ago, ABI Research reported that machine vision systems incorporating deep learning are more flexible than earlier generation products, meaning that one system can “recognize many object types and be deployed in a range of circumstances.”
Before deep learning adoption, “Machine vision vendors relied on hardcoded feature detection techniques, which meant they could only be applied in highly controlled environments, such as inspecting a single type of object on a production line,” ABI Research explained in its news release.
In addition to prototype and manufacturing support, the collaboration between Ambarella and eInfochips brings the following services to IoT device developers using Ambarella solutions:
• Multi-sensor camera designs and video analytics
• Camera SDK and system software development
• Image sensor tuning
• AI and machine learning algorithm porting and machine vision enablement
“Our relationship will give developers access to the deeply experienced design team at eInfochips, who have received intensive training from Ambarella’s experts on our CVflow systems-on-chip, hardware development tools, software development kit, and imaging pipeline,” Day says.
Linda Wilson | Editor in Chief
Linda Wilson joined the team at Vision Systems Design in 2022. She has more than 25 years of experience in B2B publishing and has written for numerous publications, including Modern Healthcare, InformationWeek, Computerworld, Health Data Management, and many others. Before joining VSD, she was the senior editor at Medical Laboratory Observer, a sister publication to VSD.