Download Brain Power: Borrowing from Biology Makes for Low-Power Computing

B o r r o w i n g
f r o m
b i o l o g y
m a k e s
f o r
l o w
computing
b r a i n
p o w e r
—
p o w e r
Brain: GREGOR SCHUSTER/GETTY IMAGES; GAUGE: PETER DAZELEY/GETTY IMAGES
R
c o m p u t i n g
B y
ead this aloud and your inner ear,
by itself, will be carrying out at least
the equivalent of a billion floating-point
operations per second, about the workload
of a typical game console. The inner ear together
with the brain can distinguish sounds that have
intensities ranging over 120 decibels, from the roar
of a jet engine to the rustle of a leaf, and it can
pick out one conversation from among dozens in a
crowded room. It is a feat no artificial system comes
close to matching.
But what’s truly amazing is the neural system’s
efficiency. Consuming about 50 watts, that game console throws off enough heat to bake a cookie, whereas
the inner ear uses just 14 microwatts and could run
for 15 years on one AA battery. If engineers could
borrow nature’s tricks, maybe they could build faster,
better, and smaller devices that don’t literally burn
holes in our pockets. The idea, called neuromorphic
engineering, has been around for 20 years, and its
first fruits are finally approaching the market.
The likely first application is bionics—the use of
devices implanted into the nervous system to help
the deaf, blind, paralyzed, and others. There are two
reasons for this choice: the biological inspiration
R a h u l
S a r p e s h k a r
crosses over to the application, and the premium on
energy efficiency is particularly important.
Bionic ears are a case in point. Today’s device,
called a cochlear implant, consists of an implanted
electrode array; a bulky, power-hungry digital-signal
processor worn outside the ear; and a wireless link
that conveys data and power to the implanted electrodes. In the near future, these devices will be fully
implanted inside the body so that deaf people will be
indistinguishable from everyone else in both appearance and, we hope, ability to hear. In the past year,
my lab at the Massachusetts Institute of Technology
has completed work on a bionic-ear processor that
does the job of the digital-signal processor, is small
enough to be implanted, and could run on a 2-gram
battery needing a wireless recharge only every two
weeks [see illustration, “Mimicking the Ear”]. As the
best batteries currently available can be recharged
about 1000 times, this device is the first to permit
30-year operation without surgery to replace the
battery. Last year, a deaf woman replaced her conventional processor with ours, though it was not
implanted, and afterward she could understand
speech easily and well.
Neuromorphic engineering and, more generally,
www.spectrum.ieee.org IEEE Spectrum | May 2006 | NA
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biologically inspired electronics are still in their infancy, but
practitioners have already accomplished amazing things [see
table, “Leading Labs”]. These include the attempt to understand
biological systems, such as the retina of the human eye and the
sonar systems of bats, by modeling them in microchips. Some
of the lessons learned have been turned to practical purposes—
for instance, applying the principles of vision in the housefly
to the control of robotic motion and designing radio-frequency
spectrum analyzers that mimic the architecture of the human
inner ear. Some devices now measure oxygen saturation in the
blood with sensors and processors inspired by the photoreceptors in our eyes; others employ pattern-recognition circuits
that rely on the mix of analog and digital features found in
the brain.
One of biology’s big power-saving secrets is that it relies on the
physics of special-purpose structures, such as ears and eyes, to
do a lot of analog computing. Ears, for example,
are complex structures that by their inherent physics alone perform filtering, frequencyspectrum analysis, and signal compression—all
before the signals are transmitted to the brain.
Many of the initial insights into biology’s computing efficiency originated with Carver Mead,
professor emeritus at the California Institute of
Technology, in Pasadena—the founding father of
neuromorphic engineering.
But ears, eyes, and even individual brain
cells also have a digital aspect. Brain cells, or
neurons, can be viewed as special-purpose
analog-to-digital converters. They recognize
particular patterns of voltage inputs from other
neurons, integrate these signals in an analog
manner, and then output a digital-like signal,
a voltage spike (1) or its absence (0). Output
spikes from one neuron act as inputs to the
next neuron. And this simple process, amplified and repeated by billions of interconnected
neurons, leads to movement, hearing, thought, and everything
else under our brain’s control [see the sidebar, “Computing With
Spikes,” which accompanies the online version of this article].
Analog devices in the ear, such as the eardrum and the cochlea,
process sound. The ear then digitizes the processed sound signal
by encoding it as spikes of voltage that travel down the auditory
nerve to the brain, which interprets the spikes to distinguish
a jazz tune from an oncoming train or a whisper. Because the
ear has already done a great deal of analog computation on the
sound, the information it provides the brain is more compact
and far better suited than raw sound to human tasks, such as
understanding what a child is whispering in a crowded movie
theater. This scheme of low-power analog processing followed
by digitization is one of the most important lessons biology has
to teach designers of electronics.
The advantage analog computers have is in how they use their
transistors and other electronic components. Whereas digital
devices use transistors as simple switches, analog systems recognize that transistors are complicated things with physical
properties that you can compute with. The use of a transistor’s
real physical relationship to current and voltage, essentially all
the shades of gray between digital’s black and white, should let
analog computers calculate with much greater efficiency than
digital ones. But there are two important limits.
An analog system beats a digital one in efficiency only if the
analog system doesn’t have to do very precise calculations or
output its answer at a high bandwidth. Precision basically means
being able not only to compute 2 + 2 to get 4 but also to calculate 1.9866235 + 2.0133765 to get 4.0000000. For an analog system,
precision is related to how a device’s performance varies with
temperature fluctuations, power-supply noise, slight differences
among individual transistors, and inherent and
wholly unavoidable fluctuations in the flow of
current and thermal noise.
Analog computing’s problem with precision is best illustrated by comparing an analog adder to a digital adder. To sum using an
analog device, just add the currents entering
a particular part of a circuit. To add 4 to 5,
put 4 milliamperes on one line, 5 milliamperes
on another, and join the two. But notice that
both the inputs and the output would have to
be accurate to at least a milliamp to get the
right answer.
A digital adder needs more circuitry, and
thus more power, to operate, but it does not
require such high accuracy. An 8-bit number
would be represented by eight wires, each carrying either a digital 1 or 0. The logic circuits
that add the numbers need only be precise
enough to tell a 1 from a 0. Adding bigger or
more precise numbers, say a 16-bit number, in
a digital adder means just doubling the number of 1-bit wires
leading to a similarly increased set of logic circuits. But such an
upgrade is much more difficult when adding with analog signals.
In fact, it would involve the circuit’s going from milliampere
accuracy to less than a microampere of accuracy.
Analog computing’s other problem, bandwidth, is caused by
that unavoidable buzz, thermal noise. In order to eliminate its
effect on, say, the answer to an addition problem, analog computers average the answer to a calculation over a period of time.
Trying to compute more quickly means less time for averaging
and more chance the answer will be corrupted by thermal noise.
So digital processing certainly has its advantages. It is common
today, when working with signals from the real world, to convert
the signal immediately into a torrent of digital bits using a fast
and highly precise analog-to-digital converter and then to do all
the subsequent processing with lots of watt-munching digital
computations. The processor then spits out a smaller stream of
bits that are meaningful to the computer or other device whose
job it is to interpret the signal. For example, a typical 16-bit audio
converter in a digital voice recorder might churn out 352 kilobits
per second of digital data. After lots of digital processing, the
signal might result in just 5 kb/s of useful speech data.
What these numbers demonstrate is the inefficiency of turning analog signals into digital bits and running digital processing
algorithms on them. The fewer bits that need to be converted
and processed, the better. As we noted earlier, nature’s solution
By now you might be wondering: if analog computing is
so marvelously efficient, why is almost every electronic system
you come across digital? One obvious reason is digital’s relative immunity to noise. With only two possible values, 1 and 0,
noise and other vagaries of the electronic world are unlikely
to alter a signal. What’s more, digital systems are robust; they
are insensitive to changes in temperature, to noise from their
power supplies, and to variations among their main constituent
parts—transistors. Digital systems also tend to be relatively easy
to program and scale up.
26IEEE Spectrum | May 2006 | NA www.spectrum.ieee.org
bryan christie design
The inner
ear uses
just 14
microwatts
and could
run for
15 years
on one AA
battery
Cochlear nerve
Transmitter
Implanted
electrodes
Implanted
receiver
Cochlea
Microphone, A/D,
and DSP
Mimicking the Ear
The ear is a fantastically efficient processor,
doing the equivalent of more than a billion
calculations per second of filtering and
compression using just microwatts
of power. Sound at the eardrum is
transferred to the cochlea—a coiled,
tapered tube with a membrane that
divides the tube along its length.
High-frequency sounds resonate in
the membrane at the start of the tube,
and low-frequency ones resonate at its
end. Sensory cells detect the vibration,
and the neurons of the cochlear nerve turn
it into a pattern of voltage spikes the brain
can understand.
COCHLEAR IMPLANTs today
The external portion of today’s cochlear implants consists of a microphone, an analog-to-digital
converter (A/D), and a digital-signal processor (DSP) [left]. Sound is digitized and then processed
into a signal that specifies which part of the cochlear nerve to stimulate and how intense the
stimulation should be. That signal is wirelessly transmitted through the skin to an electrode array
implanted in a person’s cochlea [above]. Cochlear implants consume a relatively large amount
of power, because they digitize much more data than is needed, all of which the digital-signal
processor must filter and compress.
Analog-to-digital
converter
Digital-signal
processor
5 mW
analog bionic ear
0.25 mW
Band-pass filters
Microphone
Amplifier
Envelope
detectors
Gain
control
To make a cochlear implant small and energy efficient enough to be fully
implanted, we redesigned it. The bionic ear processes with low-power analog
components before digitizing only a small amount of signal. Sound entering
a microphone is amplified, and a gain-control circuit adjusts its range of
Scanners
Output bits control
electrode current
intensity. The signal is then split into 16 channels (only four are shown). On
each channel a band-pass filter lets only a defined set of frequencies through.
Envelope detectors calculate the amount of signal at each frequency, and the
result is digitized. Scanners then combine the data from all the channels.
is to first process the incoming analog information efficiently
with interconnected, special-purpose analog devices—eardrums,
cochleas, and sensory cells, for instance—and delay the analogto-digital conversion until after this processing has reduced the
amount of information needing to be digitized. For example,
rather than report just the intensity of the light falling on each
of millions of cells in your retina, interconnected neurons in your
eye use analog processes to calculate where the edges in an image
lie and encode that data as spikes of voltage on your optic nerve.
So how do organs such as the inner ear deal with the imprecision
of analog computing? Indeed, many biological components, such as
neurons in the brain, are by themselves low-precision components.
But in complete organs such as an ear or eye, a great many imprecise analog processing elements interconnect and deliver amazingly
accurate information. For a bat to find a bug by echolocation, its
auditory system must be able to discern a difference in the time in
which sound arrives on the order of tens to hundreds of nanoseconds. But all the neurons involved in that process are accurate down
only to the millisecond. Just as the digital adder discussed earlier
combines many 1-bit calculations to get an 8-bit answer, biological
processors tie together many imprecise analog computational units
with a combination of analog and digital interactions.
One of the strengths of digital systems is that they tend to be
robust, right down to the transistors that constitute them. We
can’t say that about biological components, but our ears, eyes,
and everything else work perfectly well in all kinds of situations.
Rather than all of the system’s components being robust as they
would be in a digital system, living things become more robust
through a combination of adaptation and learning. For sensors
such as the eye, robustness means constantly adapting using
feedback from the incoming information. When a light brightens, for example, our pupils contract to let less light in. At the
same time, the eye’s photoreceptors can quickly recalibrate their
own sensitivity to compensate for the brightness. Finally, the
system that includes the brain and eyes can learn. For example,
we learn to compensate for the way our heads move when we
walk, by moving our eyes in the opposite direction, so the world
isn’t just a big blur.
The starting point for making low-power circuits that com-
pute like your eyes and ears is the transistor itself. There was
no need to come up with something new, as the metal oxide
semiconductor (MOS) transistor—the kind found in most chips
today—has an analog personality.
In normal use, MOS transistors are treated as simple switches
that are either all on or all off. But in fact, the current passing
through the transistor is actually a smooth but very steep exponential function of the control voltage. Even when the transistor is off,
you see tiny amounts of what is called subthreshold current. And
this tiny current can be controlled and used in computation.
Subthreshold circuits were pioneered largely by Professor
Eric Vittoz of CSEM (the Swiss Center for Electronics and
Microtechnology), in Neuchâtel, and are in widespread use in the
watch and pacemaker industries, where saving power is paramount.
The trouble with such circuits is their sensitivity: a small change in
either voltage or temperature can drastically alter the current they
transmit. Engineers solve the problem by carefully designing the
circuits to regulate the currents going through the transistors.
Subthreshold transistors share an interesting characteristic with brain cells that makes them even more attractive for
building neuromorphic chips. The relationship between subthreshold current and the voltage controlling it resembles the
current-to-voltage relationships neurobiologists see in molecu-
28IEEE Spectrum | May 2006 | NA lar structures on the surface of brain cells. These structures,
called ion channels, are the main means by which brain cells
communicate. Channels on one cell open in response to a voltagecontrolled chemical signal from an adjacent cell, allowing ions
to flow into or out of the cell. This flow ultimately changes the
cell’s voltage.
Using subthreshold circuits and the biological manner
of computing efficiently and robustly, my laboratory has constructed a bionic ear to restore hearing in deaf people [again, see
illustration, “Mimicking the Ear”]. To understand how the processor works, it helps to first understand how a real ear works.
Sound enters the ear and causes the eardrum to vibrate. The
vibration is transferred by a set of tiny bones to the cochlea, a coiled,
tapered tube divided by a membrane along its length into two main
fluid-filled compartments. The vibration begins in the membrane at
the wider end of the tube and travels toward the narrow end. Because
of the stiffness of the cochlea’s tapered membrane, high-frequency
sounds resonate at the start of the tube and low-frequency ones
resonate at its end. So, by purely mechanical means, the cochlea
divides sound into its spectrum of component frequencies.
Sensory organs, called outer and inner hair cells, sway with
the resonance. The outer hair cells amplify the vibration electromechanically. The inner hair cells secrete chemicals as a
consequence of their motion, which cause neighboring neurons
to generate voltage spikes that finally convey a filtered and compressed version of the sound to the brain. In a sense, the neurons
are performing analog-to-digital conversion.
The hair cells and neurons of the ear also have a gain-control
function. That is, even though the incoming sound varies by over
12 orders of magnitude—from a whisper to a jet engine—the
rate of voltage spikes to the brain varies by only 3 or 4 orders of
magnitude. It is, all together, a marvelous combination of fluid
mechanics and neural electronics.
Cochlear implants, or bionic ears, are not exact copies of the
ear, but they do restore hearing in profoundly deaf people. Like
the ear, they break sound into its component frequencies, then
electrically stimulate the neurons in the cochlea that are specialized to pick up those frequencies. The responsibility for gain
control is shifted from the hair cells and neurons to the device.
The analog bionic ear we’ve developed at MIT electronically
mimics some aspects of how the ear processes sound and follows
biology’s general low-power strategy of delaying digitization
until it’s both necessary and energy efficient. First, a microphone and preamplifier turn sound into an analog electronic
signal. That signal passes through an automatic gain-control
circuit, which narrows the range of intensity. A 16-channel spectrum analyzer then parses the signal into frequency components.
Each channel of the analyzer has three parts—a band-pass filter
that lets only a predetermined chunk of frequencies through, an
envelope detector circuit that calculates the signal energy at each
frequency, and a converter that computes the logarithm of the
energy and turns it into a digital value. The output bits are then
used to control the intensity of the current passed into each of
16 electrodes implanted in the nerves of the cochlea.
All of that processing draws a frugal 250 μW of power, leaving
750 μW of the total 1 milliwatt power budget for stimulating the
nerves. The processing power is one-twentieth of that in today’s
cochlear implants.
The design compensates for analog processing’s twin problems of
precision and bandwidth by compressing the sound signal so much
that the analog-to-digital converters need only handle data of a
modest 7-bit precision and at most a few kilohertz of bandwidth.
www.spectrum.ieee.org
Making the circuit robust required several innovations in
the chip’s building-block analog circuits. These allowed the
circuits to operate over a wide range of frequencies at very low
power while remaining immune to the effects of temperature,
variations in the individual transistors, and power-supply noise.
Again, as in the biological model, the chip handles variations
in the environment, because the overall system allows for the
sensitivity of its many parts by constantly recalibrating its components, using feed-forward and feedback signals.
Our bionic ear chip uses several circuits we built into an
earlier device, a simplified electronic model of the cochlea made
of silicon. The silicon cochlea has also led to the design of a new
algorithm that can improve the performance of ordinary cochlear
implants and other speech processors.
The algorithm mimics one of the cochlea’s more interesting
behaviors: strong signals in one frequency band tend to suppress weaker signals in neighboring bands. Thus, your inner ear
quashes interfering noise before it reaches your brain. However,
as with the real cochlea, the algorithm ensures that weak signals in frequency bands that are distant from strong signals are
still audible. Other researchers have programmed our algorithm
into the cochlear-implant processors of a few deaf people and
have shown that it helped improve the accuracy of their understanding of speech in the presence of background noise. It also
improved speech recognition in systems that are meant to help
users understand what’s said in cars and other noisy places.
Taking another lesson from the cochlea, we are designing
an ultra-wide-band spectrum analyzer that can simultaneously tune into radio signals all the way from the FM radio
(around 100 MHz) bands to Wi-Fi (less than 10 GHz) bands.
In designing the device, we’re trying to do with silicon what
the cochlea does with its exponentially tapered membrane—
namely, perform spectral analysis over a hundredfold range of
frequencies significantly faster than a conventional spectrum
analyzer would.
While we’ve taken the lesson of low-power analog pro-
cessing prior to digital conversion to heart, there are three
fundamental things that we still need to discover about biological systems to make engineering marvels that rival them.
First, we need a better understanding of how they perform
efficient, reliable computations with noisy, unreliable devices
in large-scale systems.
Second, we want to learn how biological systems operate at
many timescales and over many length scales. The brain, for
example, is made up of a network of neurons with positive and
negative feedback loops that operate in as little as milliseconds
or as much as days and over connection distances ranging from
micrometers to centimeters. An equivalent system built with
the best engineering strategies for complex system design today
would likely be highly unstable.
Finally, we need to replicate the ability of a cell to process a
great many converging inputs and to produce output that influences a great many other cells. Advanced digital and analog
architectures today are just starting to copy the massively parallel architectures of neurobiology. But the parallelism we can
achieve today is limited by the paltry number of interconnections we can fit on a chip.
Neuromorphic researchers have so far picked biology’s lowhanging fruit. There are many systems vastly more complex than
ears and eyes that do amazing computation on very little power.
In fact, the network of chemical interactions in just a single
human cell forms an awe-inspiring computer that regulates the
cell’s growth, structure, repair, and reproduction. The organization of such a system may one day serve as inspiration to create
complex networks of computers that perform tasks we cannot
even conceive of yet. As Richard Feynman, a great physicist of
the previous century, once said, “The imagination of nature is
far, far greater than the imagination of man.” n
ABOUT THE AUTHOR
RAHUL SARPESHKAR is an associate professor in
electrical engineering at the Massachusetts Institute
of Technology, where he heads the Analog VLSI and
Biological Systems lab.
TO PROBE FURTHER
For an in-depth description of the bionic ear, see “An UltraLow-Power Programmable Analog Bionic Ear Processor,” by
Rahul Sarpeshkar et al., IEEE Transactions on Biomedical
Engineering, April 2005, pp. 711–727.
Carver Mead, neuromorphic engineering’s founding
father, described its goals and approach in “Neuromorphic
Electronic Systems,” Proceedings of the IEEE, October 1990,
pp. 1629–1636. More recently, Proceedings devoted a July
2001 article to neuromorphic engineering.
Leading Labs
VISION and ROBOTICS
Geoffrey Barrows (Centeye Inc.); Kwabena Boahen
(University of Pennsylvania); Tobi Delbruck, Shih-Chii Liu,
and Giacomo Indiveri (ETH Zurich); Ralph EtienneCummings (Johns Hopkins University); Nicolas
Franceschini (CNRS, France); Reid Harrison (University
of Utah); Charles Higgins (University of Arizona); Timothy
Horiuchi (University of Maryland); Rahul Sarpeshkar
(Massachusetts Institute of Technology); Bertram Shi
(Hong Kong University of Science and Technology); Steve
de Weerth (Georgia Institute of Technology)
HEARING, SONAR,
and SPEECH PROCESSING
Andreas Andreou (Johns Hopkins); Gert Cauwenberghs
(University of California, San Diego); Timothy Horiuchi
and Shihab Shamma (University of Maryland); Rahul
Sarpeshkar; Andre van Schaik (University of Sydney)
NEURAL PROCESSING and LEARNING
Gert Cauwenberghs; Chris Diorio (University of
Washington); Paul Hasler (Georgia Tech); Rahul
Sarpeshkar
SPIKE-BASED PROCESSING
Kwabena Boahen; Alan Murray and Alister Hamilton
(University of Edinburgh); Tor Sverre Lande (University of
Oslo); Shih-Chii Liu; Rahul Sarpeshkar
BIOMEDICAL and ULTRA-LOW-POWER
APPLICATIONS
Reid Harrison; Rahul Sarpeshkar; Christofer Toumazou
(Imperial College)
www.spectrum.ieee.org IEEE Spectrum | May 2006 | NA
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