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Emza and Alif Demonstrate Fast, Ultra-Efficient Object Detection for Tiny AI Edge Devices

Emza Visual Sense, a pioneer in Tiny AI visual sensing, is joining with Alif Semiconductor to show how the combination of powerful, highly efficient Arm®-based hardware and optimized models can make AI a reality at the edge. The companies are demonstrating Emza’s trained face detection model running on Alif’s Ensemble™ microcontroller (MCU), the first MCU featuring the Arm Ethos™-U55 microNPU. The Emza model runs an order of magnitude faster on the Ensemble device with Ethos-U55 compared to a CPU-only solution, with extremely low power consumption.

The combination of AI/ML frameworks, models, neural networking processors (NPUs) and silicon that are all optimized for edge AI solutions means it’s now possible to support complex AI inference capabilities such as eye tracking and facial identification in low-power, low-cost devices. This creates an opportunity for creation of new applications and use cases across industrial IoT devices, consumer appliances, and numerous other segments.

“To unleash the potential of endpoint AI, we need to make it easier for IoT developers to access higher performance, less complex development flows and optimized ML models,” said Mohamed Awad, vice president of IoT and Embedded at Arm. “Alif’s MCU is helping redefine what is possible at the smallest endpoints and Emza’s contribution of optimized models to the Arm AI open-source repository will accelerate edge AI development.”

Emza trained a sophisticated, full implementation of a face detection model on the Arm Ethos-U55 microNPU and is the first Arm AI ecosystem partner to contribute a complete application code ML example to Arm’s ML Embedded Eval Kit repository. Companies can use it to gauge runtime, CPU demands, memory allocation and other requirements even before silicon is available.

“Emza’s powerful visual sensing technology is already shipping in millions of products, and we’re excited to bring our optimized algorithms to the wider universe of SoC vendors and OEMs,” said Yoram Zylberberg, CEO, Emza. “As we look at the dramatically expanding horizon for TinyML edge devices, Emza is focused on enabling new applications across a broad array of markets. There is virtually no limit to the types of visual sensing use cases that can be supported by new powerful, highly efficient hardware.”

“In creating low-power, always-sensing IoT devices with AI/ML capability, it’s clear that we need to take every opportunity for optimization, including innovations in MCU design,” said Reza Kazerounian, President & Co-Founder, Alif Semiconductor. “Our Ensemble devices leverage one of the latest generation processor cores and neural network accelerators from Arm in highly scalable configurations. On top of that, we layer Alif’s unique technologies, low-power design techniques, deep embedded security, and a high level of functional integration – all resulting in extremely capable, secure devices with low power consumption and long battery life. We’re delighted to work with Arm and Emza to showcase the capabilities of these devices and empower the next generation of edge AI applications.”

Availability
The full face detection project example for the Arm Ethos-U55 is available NOW.
Other Emza object detection models are also available: https://github.com/emza-vs.

The Arm Ethos-U55 microNPU is available now:
https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u55.

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