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Emza Visual Sense and Alif Semiconductor demonstrated an optimized face detection model running on Alif’s Ensemble microcontroller based on Arm IP. Both have found it suitable for enhancing low-power artificial intelligence (AI) at the edge.
The emergence of optimized silicon, AI and machine learning (ML) models and frameworks has made it possible to perform advanced AI inference tasks such as eye tracking and face identification at the periphery, low power consumption and low cost. This opens up new use cases in areas such as industrial IoT and consumer applications.
Manufacture of peripheral devices faster magnitudes
Using Alif’s Ensemble Multipoint Control Unit (MCU), which Alif says is the first MCU using the Arm Ethos-U55 microNPU, the AI model ran “an order of magnitude” faster than a solution. only CPU with the M55 at 400 MHz. It looks like Alif meant two orders of magnitude, as the footnotes say the high-performance U55 took 4ms versus 394ms for the M55. The high-output U55 ran the model in 11ms. The Ethos-U55 is part of Arm’s Corstone-310 subsystem, for which it launched new solutions in April.
Emza said it trained a comprehensive “sophisticated” face detection model on the NPU that can be used for face detection, face yaw angle estimation, and facial landmarks. The complete application code has been added to Arm’s open source AI repository called “ML Embedded Eval Kit”, making it the first partner in the Arm AI ecosystem to do so. The benchmark can be used to assess execution time, CPU demand, and memory allocation before silicon becomes available.
“To unleash the potential of endpoint AI, we need to make it easier for IoT developers to access higher performance, less complex development workflows, and optimized ML models,” said Mohamed Awad. , vice president of IoT and embedded at Arm. “Alif’s MCU is helping redefine what’s possible at the smallest endpoints and Emza’s contribution of optimized models to the Arm AI open source repository will accelerate the development of cutting-edge AI.”
Emza says its visual sensing technology is already available in millions of products and with this demonstration it is extending its optimized algorithms to SoC vendors and OEMs.
“As we examine the dramatically expanding horizon of TinyML edge devices, Emza is focused on enabling new applications across a broad range of markets,” said Emza CEO Yoram Zylberberg. “There are virtually no limits to the types of visual sensing use cases that can be supported by powerful and highly efficient new hardware.”
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