Microsoft has previewed ‘Project Brainwave’ – a hardware architecture integrated with Azure machine learning, which is designed to accelerate real-time artificial intelligence (AI) calculations.
“I think this is a first step in making the field-programmable gate arrays (FPGA) more of a general-purpose platform for customers,” said Mark Russinovich, chief technical officer for Microsoft’s Azure cloud computing platform, in a recent blog post by Allison Linn, senior writer and editor at Microsoft.
Manufacturing services company Jabil is working with Microsoft to look at how it could use Project Brainwave to quickly and accurately use AI to scan images and flag false positives – or items that aren’t actually defective.
“It’s highly competitive, so anything you can do to get an advantage for the customer is going to provide incremental improvement,” said Ryan Litvak, IT manager at Jabil.
The Project Brainwave preview includes the ability for customers to complete image recognition for applications such as the one Jabil is piloting, and it lets people do AI-based computations in real time, instead of batching it into smaller groups of separate computations. It works on TensorFlow, one of the most commonly used frameworks for doing AI calculations using deep neural networks, a method that is roughly modelled on theories about how the brain works.
Microsoft also is releasing a limited preview to bring Project Brainwave to the edge, meaning users could take advantage of that computing speed in their own businesses and facilities, even if their systems aren’t connected to a network or the Internet.
“We’re making real-time AI available to customers both on the cloud and on the edge,” said Doug Burger, engineer at Microsoft, who leads the group that has pioneered the idea of using FPGAs for AI work.
Jabil also is looking at ways to use AI on Project Brainwave to better predict when manufacturing operations need maintenance, as a way to cut downtime.
The public preview of Project Brainwave comes about five years after Burger, a former academic who works in Microsoft’s research labs, first began talking about the idea of using FPGAs for more efficient computer processing. As he refined his idea, the current AI revolution kicked into full gear. That has created a massive need for systems that can process the large amounts of data required for AI systems to do things like scan documents and images for information, recognise speech and translate conversations.