That process is similar to the early days of game development, where programmers weren't exposed to GPUs as most didn't know how to exploit the on-chip features. Vulkan recently replaced OpenGL APIs and exposed GPU features directly to programmers, who are better equipped to exploit features on the chip.
The potential of deep learning is illustrated in self-driving cars, which use powerful computers to navigate a vehicle safely by recognizing signals, lanes, and other objects. Like chips in cars and servers, the TrueNorth chip does low-level processing on each neuron, and they are then stringed together to provide identify an object in an image, or recognize a sound. That's the technique also being used by Intel and Nvidia in their mega-chips, which are more power hungry than TrueNorth.
These are still early days for IBM's TrueNorth chip. The company plans to build a computer with these chips at the scale of a human brain, but part of the challenge is developing algorithms and applications for such a huge computer.
IBM started the development of brain-like chips in 2004 and simulated a computer model the scale of a cat's brain in 2009. A prototype chip in 2011 had 256 digital neurons and had pattern-recognition capabilities. A full computer with a brain-emulating chip could still be a long time off.
IBM is also building quantum computer as an option to replace today's PCs and servers, which are based on decades-old computer designs. Other chips that emulate human brains are being developed by Hewlett Packard Enterprise, Stanford University, the University of Heidelberg in Germany, and the University of Manchester in the U.K.
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