MIT engineers have created a reconfigurable AI chip that comprises alternating layers
of sensing and processing elements that can communicate with each other.
Imagine a more sustainable future, where cellphones, smartwatches, and other wearable devices don’t
have to be shelved or discarded for a newer model.
Instead, they could be upgraded with the latest sensors and processors that would snap onto
a device’s internal chip — like LEGO bricks incorporated into an existing build.
Such reconfigurable chipware could keep devices up to date while reducing our electronic waste.
Now MIT engineers have taken a step toward that modular vision 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.
The researcher says that we can add as many computing layers and sensors as we want, such
as for light, pressure, and even smell.
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.
As we enter the era of the internet of things based on sensor networks, demand for multifunctioning
edge-computing devices will expand dramatically.
The researcher says their proposed hardware architecture will provide high versatility
of edge computing in the future.
The team’s results are published in the journal Nature Electronics.
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.
In their new chip design, the researchers paired image sensors with artificial synapse
arrays, each of which they trained to recognize certain letters — in this case, M, I, and
T. While a conventional approach would be to relay a sensor’s signals to a processor
via physical wires, the team instead fabricated an optical system between each sensor and
artificial synapse array to enable communication between the layers, without requiring a physical
connection.
Other chips are physically wired through metal, which makes them hard to rewire and redesign,
so we have to make a new chip if we want to add any new function.
The MIT researchers replaced that physical wire connection with an optical communication
system, which gives them the freedom to stack and add chips the way they want.
The team’s optical communication system consists of paired photodetectors and LEDs,
each patterned with tiny pixels.
Photodetectors constitute an image sensor for receiving data, and LEDs to transmit data
to the next layer.
As a signal (for instance an image of a letter) reaches the image sensor, the image’s light
pattern encodes a certain configuration of LED pixels, which in turn stimulates another
layer of photodetectors, along with an artificial synapse array, which classifies the signal
based on the pattern and strength of the incoming LED light.
The team fabricated a single chip, with a computing core measuring about 4 square millimeters,
or about the size of a piece of confetti.
The chip is stacked with three image recognition “blocks,” each comprising an image sensor,
optical communication layer, and artificial synapse array for classifying one of three
letters, M, I, or T. They then shone a pixellated image of random letters onto the chip and
measured the electrical current that each neural network array produced in response.
The larger the current, the larger the chance that the image is indeed the letter that the
particular array is trained to recognize.
The team found that the chip correctly classified clear images of each letter, but it was less
able to distinguish between blurry images, for instance between I and T. However, the
researchers were able to quickly swap out the chip’s processing layer for a better
“denoising” processor, and found the chip then accurately identified the images.
The researcher says that they showed stackability, replaceability, and the ability to insert
a new function into the chip.
The researchers plan to add more sensing and processing capabilities to the chip, and they
envision the applications to be boundless.
The researcher says that we can add layers to a cellphone’s camera so it could recognize
more complex images, or makes these into healthcare monitors that can be embedded in wearable
electronic skin.
Another idea, he adds, is for modular chips, built into electronics, that consumers can
choose to build up with the latest sensor and processor “bricks.”
The researcher explains that we can make a general chip platform, and each layer could
be sold separately like a video game.
We could make different types of neural networks, like for image or voice recognition, and let
the customer choose what they want, and add to an existing chip like a LEGO.