The next generation of artificial intelligence may be 1000 times more energy efficient than current programs, thanks to computer chips that work like the human brain.
Artificial intelligence makes video games more realistic and helps your phone recognize your voice—but the power-hungry programs slurp up energy big time. However, the next generation of AI may be 1000 times more energy efficient, thanks to computer chips that work like the human brain. A new study shows such neuromorphic chips can run AI algorithms using just a fraction of the energy consumed by ordinary chips.
A common component of such networks is a software unit called long short-term memory , which maintains a memory of one element as things change over time. A vertical edge in an image, for example, needs to be retained in memory as the software determines whether it represents a part of the numeral “4” or the door of a car. Typical AI systems must keep track of hundreds of LSTM elements at once.
The setup in a neuromorphic chip handles memory and computation together, making it much more energy efficient: Our brains only require 20 watts of power, about the same as an energy-efficient light bulb. But to make use of this architecture, computer scientists need to reinvent how they carry out functions such as LSTM.
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