Machine Design – May 2019

R

esearchers at the Geor-

gia Institute of Technol-

ogy have developed an

ultra-low-power neural-

network chip inspired by the brain that

could let palm-sized robots collabo-

rate and learn from their experiences.

Combined with new generations of

low-power motors and sensors, the new

application-specific integrated circuit

(ASIC), which operates on milliwatts

of power, could help intelligent swarm

robots operate for hours instead of min-

utes.

“We are trying to bring intelligence

to small robots so they can learn about

their environment and move around

autonomously,” says Georgia Tech

professor Arijit Raychowdhury. “To do

that, we want to let low-power circuits

in small robots make decisions on their

own. There is a huge demand for small,

but capable robots.”

To conserve power, the chip uses

a hybrid digital-analog time-domain

processor in which signals’ pulse-

width encodes information. The neural

network IC accommodates model-

based programming and collaborative

reinforcement learning, potentially

giving small robots larger capabilities

for reconnaissance, search-and-rescue,

and other missions.

In time domain computing,

information is carried on two different

voltages encoded in the width of

This chip uses a hybrid digital-analog time-domain processor that encodes information

in signals’ pulse widths.

LOW-POWER CHIP

Lets Robots Learn and Collaborate

A robotic car controlled by an ultra-low power hybrid chip. (Photo: Allison Carter, Georgia Tech)

Georgia Tech researchers have developed an ultra-low power hybrid chip inspired by the

brain that could help give palm-sized robots the ability to collaborate and learn from their

experiences. (Photo: Allison Carter, Georgia Tech)

10 MAY 2019 MACHINE DESIGN