The big idea is to get commercialised designs for a chip to help mobile processors make use of the AI method known as deep learning. Deep learning has inspired companies including Google, Facebook, and Baidu to invest in the technology, so far it has been limited to large clusters of high-powered computers.
Eugenio Culurciello, a professor at Purdue working on the project said that being able to implement deep learning in more compact and power-efficient ways could lead to smartphones and other mobile devices that can understand the content of images and video.
At the Neural Information Processing Systems conference in Nevada, the group demonstrated that a co-processor connected to a conventional smartphone processor could help it run deep learning software. The software was able to detect faces or label parts of a street scene. The co-processor’s design was tested on an FPGA, a reconfigurable chip that can be programmed to test a new hardware design without the considerable expense of fabricating a completely new chip.
While this is less powerful than systems like Google’s cat detector, but it shows how new forms of hardware could make it possible to use the power of deep learning more widely.