XMOS announces reference design solution for smart parking
UK-based XMOS launched reference design solutions for Automatic License Plate Recognition (ALPR). By combining XMOS’ xcore.ai semiconductor chip and Cloudtop’s machine learning model, this design enables reading of slow-moving license plates at a distance of 3-5 metres with high accuracy.
The new solution is designed to consume less power, and has on-device processing abilities with lower bill of materials compared to solutions which are already available in the market, which uses complex hardware, high-resolution cameras, operating on complex machine learning models that depend on cloud connectivity for image processing, according to XMOS.
“For smart parking, cloud connectivity and huge processing power is simply overkill,” commented Aneet Chopra, VP Product, Marketing & Business Development, XMOS. “It makes ALPR 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.”
“Simplicity and affordability are two priorities in the ALPR space, not only to drive sales but to encourage innovation” commented, 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.”
XMOS and Cloudtop will showcase the solution at tinyML Summit in San Francisco, between 28-30th March.