Tech Briefs Magazine - August 2022 - MD-15

Engineers Build Reconfigurable Artificial Intelligence Chip
The researchers plan to apply the design to edge computing devices.
Massachusetts Institute of Technology, Cambridge, MA
M
IT engineers are taking a modular
approach with a LEGO-like design
for a stackable, reconfigurable artificial
intelligence chip. The design comprises
alternating layers of sensing and processing
elements, along with light-emitting
diodes (LED) that allow for the chip's
layers to communicate optically. Other
modular chip designs employ conventional
wiring to relay signals between
layers. Such intricate connections are
difficult if not impossible to sever and
rewire, making such stackable designs
not reconfigurable.
The MIT design uses light, rather than
physical wires, to transmit information
through the chip. The chip can therefore
be reconfigured, with layers that can be
swapped out or stacked on, for instance to
add new sensors or updated processors.
" You can add as many computing layers
and sensors as you want, such as for light,
pressure, and even smell, " said MIT
Postdoc Jihoon Kang. " We call this a
LEGO-like reconfigurable AI chip because
it has unlimited expandability depending
on the combination of layers. "
" As we enter the era of the internet of
things based on sensor networks, demand
for multifunctioning edge-computing
devices will expand dramatically, " said
Jeehwan Kim, Associate Professor of
MIT engineers have created a reconfigurable AI chip that comprises alternating layers of sensing and processing
elements that can communicate with each other. (Image: Figure courtesy of the researchers and edited by MIT News)
Mechanical Engineering at MIT. " Our
proposed hardware architecture will provide
high versatility of edge computing in
the future. "
The team's design is currently configured
to carry out basic image-recognition
tasks. It does so via a layering of image
sensors, LEDs, and processors made from
artificial synapses - arrays of memory resistors,
or " memristors, " that the team
previously developed, which together
function as a physical neural network, or
" brain-on-a-chip. " Each array can be
trained to process and classify signals directly
on a chip, without the need for external
software or an internet connection.
The researchers are eager to apply
the design to edge computing devices
- self-sufficient
sensors and other
electronics that work independently
from any central or distributed resources
such as supercomputers or
cloud-based computing.
For more information, contact Abby
Abazorius at 617-253-2709 or abbya@
mit.edu.
An Algorithmic Planner Helps Delegate Tasks to
Human-Robot Teams
The technology could be useful in manufacturing and assembly plants for sorting packages, or in
any environment where humans and robots collaborate.
Carnegie Mellon University, Pittsburg, PA
A
s robots increasingly join people on
the factory floor, in warehouses and
elsewhere on the job, dividing up who will
do which tasks grows in complexity and importance.
Researchers at Carnegie Mellon
University's Robotics Institute have developed
an algorithmic planner that helps
delegate tasks to humans and robots. The
planner, " Act, Delegate or Learn " (ADL),
Motion Design, August 2022
considers a list of tasks and decides how
best to assign them. The researchers asked
three questions: When should a robot act
to complete a task? When should a task be
delegated to a human? And when should a
robot learn a new task?
" There are costs associated with the
decisions made, such as the time it takes a
human to complete a task or teach a
robot to complete a task and the cost of a
robot failing at a task, " said Shivam Vats,
the lead researcher and a Ph.D. student.
" Given all those costs, our system will give
you the optimal division of labor. "
The team's work could be useful in
manufacturing and assembly plants, for
sorting packages, or in any environment
where humans and robots collaborate to
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Tech Briefs Magazine - August 2022

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