Page 38 - Security Today, September/October 2021
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There is no doubt that image classification within security appli- cations is evolving with AI. Moving from pixel-based algorithms in video motion detection to ML and DL models that can clas- sify people and vehicles is a start. What’s more, a reduction in false positives can be attributed to the improvement of many DL models through real-world data.
Devices with a custom ASIC, DLPU or a SOC designed and optimized for DL will provide advantages at the edge. Edge de- vices with hardware acceleration for ML or DL will offer better performance and efficiencies. As AI becomes more mainstream, open-source projects will fuel the growth in edge-based pro- cessing along with some proprietary technologies around Deep Learning. For example, Google’s Tensor Processing Unit or TPU is an AI accelerator ASIC that was developed in 2015 specifically for NNET Machine Learning.
Google opened licensing availability of the TPU to third parties in 2018 to further advance the adoption of DL to other hardware manufacturers. Their Edge TPU was designed around a low power consumption draw of 2W compared to their server based TPUs. The Edge TPU in its current generation can process 4 trillion op- erations per second and offers an alternative to GPU accelerated Machine Learning. This is just one example of the innovations in DL hardware acceleration that can lead to breakthroughs in AI and edge compute devices that are processing images in real-time.
The future for DL on edge devices will be dependent on how ef- ficient an ASIC, DLPU, or SOC design is implemented.
Artificial intelligence has already begun to impact the security industry, and it has promising and exciting implications. Intel- ligence is transitioning to a distributed architecture that impacts edge devices directly where data is collected. Increasingly, more AI-experienced companies are collaborating with customers and partners in our industry. Many companies are investing and ex- ploring AI-centric solutions and are looking for partners to work with in the process. AI-based solutions in our industry will not be a one size fit all and will require a team well-versed in AI frame- works.
These teams must be willing to challenge conventions and ask hard questions in order to get to the root of a problem before architecting a solution around AI. With recent advancements and new opportunities, there’s no doubt that innovations in AI will grow exponentially in the coming years—and
these innovations will transform our industry
and redefine the future of public safety, opera-
tional efficiency and business intelligence.
Quang Trinh is the manager of professional services at Axis Communications.
Motion Transmitter and Portable Receiver
Untitled-4 1
•Detects people or vehicles •Monitor customer traffic
•For use in a retail shops, garden centers
Or at home
• 1 Mile range from transmitter to receiver

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