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Low cost, low power reference design for smart parking 

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By Nick Flaherty


UK microcontroller developer XMOS has launched a reference design for Automatic Number Plate Recognition (ANPR) in smart parking systems. 

The reference design can read slow-moving license plates at a distance of 3-5 metres with high accuracy. The design is based around the xcore.ai microcontroller running an embedded machine learning model from Cloudtop. 

This avoids the need for high-resolution cameras with GPUs running machine learning models that depend on cloud connectivity for image processing. 

“For smart parking, cloud connectivity and huge processing power is simply overkill,” said Aneet Chopra, VP Product, Marketing & Business Development at XMOS, based in San Jose, California. “It makes ALPR (automatic license plate recognition) networks far more expensive than they need to be, makes maintenance more complex, and comes rife with privacy concerns inherent to the cloud. 

“The reference design we’ve developed eliminates those issues simply by streamlining the process. If you can deliver the intelligence and power you need on-device, you avoid sending all raw data to cloud, or excessively expensive or powerful hardware. That’s only going to help us drive progress in ALPR in the long run,” he said. 

“Simplicity and affordability are two priorities in the ALPR space, not only to drive sales but to encourage innovation” said Prof Zhang, Co-founder of Cloudtop. “Making devices cheaper, simpler and more reliable will be hugely important for the smart city, and downscaling machine learning models so that they can run on mass-producible silicon like xcore.ai affords developers the funding and design flexibility to experiment.” 

www.xmos.ai/alpr 

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