Ambarella, elnfochips Collaborate on Edge AI Vision Services

Edge AI semiconductor company Ambarella and digital transformation and product engineering services provider elnfochips will collaborate to expand design and development services for AI camera products based on Ambarella’s CVflow Edge AI system-on-chip (SoC) platform.

eInfochips already offers end-to-end design and development services based on Ambarella’s CVflow Edge AI SoC families, and, according to the companies, the newly formalized relationship will benefit from eInfochips’ engineering experience and resources to support the growth of AI IoT applications.

These applications include perception for the robotics, access control, commercial, security, video conferencing, and health care markets.

In addition to prototype and manufacturing support, the collaboration delivers eInfochips’ product design and development services for Ambarella-based camera solutions, including multisensor camera designs and video analytics; camera software development kits and system software development; image sensor tuning; and AI and machine learning algorithm porting and machine vision enablement.

According to Lian Jye Su, research director for AI at technology intelligence research firm ABI Research, the collaboration addresses a high-growth market by supporting a number of IoT applications — in addition to those that the companies cited.

“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,” Su said. “The combination of Ambarella’s CVflow AI processor families with eInfochips’ significant perception design expertise in a wide variety of IoT applications will help drive the rapid AI growth in this market.”

ABI projects over 12% penetration of deep learning into the AI IoT end-point market by 2028.

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Published: November 2022

Glossary

machine vision: Machine vision, also known as computer vision or computer sight, refers to the technology that enables machines, typically computers, to interpret and understand visual information from the world, much like the human visual system. It involves the development and application of algorithms and systems that allow machines to acquire, process, analyze, and make decisions based on visual data. Key aspects of machine vision include: Image acquisition: Machine vision systems use various...
computer vision: Computer vision enables computers to interpret and make decisions based on visual data, such as images and videos. It involves the development of algorithms, techniques, and systems that enable machines to gain an understanding of the visual world, similar to how humans perceive and interpret visual information. Key aspects and tasks within computer vision include: Image recognition: Identifying and categorizing objects, scenes, or patterns within images. This involves training...
edge ai: Edge AI, also known as edge artificial intelligence, refers to the deployment of artificial intelligence (AI) algorithms and models directly on edge devices, such as smartphones, sensors, IoT devices, and embedded systems, rather than relying solely on centralized cloud computing resources. On-device processing: Edge AI involves running AI algorithms and models directly on edge devices, enabling real-time data processing and decision-making without requiring data to be sent to a...
deep learning: Deep learning is a subset of machine learning that involves the use of artificial neural networks to model and solve complex problems. The term "deep" in deep learning refers to the use of deep neural networks, which are neural networks with multiple layers (deep architectures). These networks, often called deep neural networks or deep neural architectures, have the ability to automatically learn hierarchical representations of data. Key concepts and components of deep learning include: ...

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