AI Processors: A New Entrant
Processors have been around for decades and have diversified according to technological advancement and customer demand. There are hundreds of different processors and variants designed and built for specific purposes; from microcontrollers, microprocessors, digital signal processors, embedded processors and media processors.
CPU processors may contain embedded graphics along with hyperthreading and multiple cores for the home and business market and designed around compact efficient design. Industrially, large PCI cards with hundreds of cores are available to companies that need to perform big data calculations such as for assessing oil well behavior. The main difference no matter the platform for CPU’s either in personal computers or servers and big data applications is the ability to check parity and error in data calculations, while all designs desire to be more energy efficient they are based upon the requirement to complete relatively diverse calculations.
GPU’s are designed to process smaller calculations faster through multiple threads specifically designed for their purpose of delivering a visual output. Artificial intelligence neural nets require similar low-level calculations and workflows. When artificial intelligence was initially being matured in the research sector GPU’s were used for this purpose with bespoke server rack based GPU systems created for larger artificial intelligence neural nets. Taking this technology further and into today has led to the development of AI processors that are extremely quick for neural net calculations yet have a lower energy footprint. Unlike GPU orientated solutions that take up unnecessary large spaces in a device these suit integration in small edge devices.
AI Processor Form Factors
AI Processors can due to their low power and small size be mounted on customized M.2 or mini-PCI cards for example allowing easy manufacturing through design for assembly practices or added to a mainboard directly should it be needed.
AI Processor Performance and Applications
AI processors can be used to process high resolution streams in real-time allowing for the best performance to be gained from high end sensors. These deep learning chips are useful in self-driving and assisted driving car designs, traffic monitoring systems, and human identification activities. This is because they allow for low-latency, low power automotive grade processors and allowing for real-time assessment. This is great for when you need decisions to be made in an instant such as for stopping or directing vehicles, assess defects on a production line or quickly identifying a person in a crowd.
These deep learning chips are fully programmable through SDK support tools. The additionally great feature of these processors is that they allow software stacks to be integrated seamlessly with existing ML development frameworks. You can use your product solution or run an out of the box solution that is ready to run with just a few customizations; this saves you time getting the product to market. These out of the box solutions are extensive and available because many AI tasks are fundamentally similar in nature; why redesign something you don’t need to?
In their usage edge devices may require offline processing of data such as in automotive applications or remote devices in IoT systems where the remit has been to increase the intelligence of more independent devices to reduce unnecessary communication. This reduces device power requirements and enhance security through not continually contacting other devices for a connection. In security cameras, does the home or business site have an intruder or an animal setting off your sensor? AI processors mean you don’t have to keep checking your cameras or worrying about monitoring the system once it has been installed.
The Future of AI
Integrated onboard or modular card AI processors in edge devices with passive cooling techniques can be used for real-time offline or intermittently connected devices making them great in home, manufacturing and automotive sectors. Additionally, these can manage traffic or city events using less cameras and active management from users; saving money on management overheads of municipal services. Whatever the problem that needs to be solve the small form-factor, energy efficiency and passive cooling greatly extends the range of AI technology in a multitude of applications.
