ANALOG VERSUS DIGITAL
Rahul Sarpeshkar
Robert J. Shillman Associate Professor
MIT
Electrical Engineering and Computer Science
6/10/04
A PRACTICAL APPLICATION
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4
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The charge from the electrode
stimulation pulses is conducted to the
spiral ganglion cell and activation
occurs.
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2
THE BIONIC EAR
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OVERALL ARCHITECTURE OF
ANALOG BIONIC EAR PROCESSOR
57dB Internal
Dynamic Range
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A-to-D
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FG3329
AFE
77dB Overall
Dynamic Range
Bias Circuit
AGC
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Visibility
Followers
Programmability
DACs
7-bit number
1kHz
Output
bits
Bionic Ear Processor Chip
211mW total power consumption including a 96mW 80dB microphone front end. Ultra-Lowpower consumption unbeatable even at the end of Moore’s law 10-20 years in the future
because mike plus A-D-then-DSP approach will consume more than 211mW even if DSP
power consumption is 0. Effective operation is at 5mW per MIP versus DSP’s 250mW per
MIP.
SUMMARY
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Digital: Robust in every signal (1 or 0) and in every device (just a switch) at the price of
not exploiting available degrees of freedom for computation. Efficient because of a
divide-and-conquer approach to computation. Most efficient at high precision.
Analog: Not robust in every signal and in every device but robust in signal-flow path if
attention is paid to reducing thermal noise, 1/f noise, power-supply noise, and device
mismatch through clever design, feedback, and calibration. Efficient because you exploit
all kinds of degrees of freedom in the signals and the devices for computation.
Inefficient because it does not take a divide-and-conquer approach to computation. Most
efficient at low precision.
Hybrid: More efficient than either because it uses a divide-and-conquer approach to
computation with constant signal restoration, while still exploiting analog degrees of
freedom to maintain efficiency. There’s an optimal amount of analog processing before
you make a signal-restoring digital decision and an optimal amount of division in the
divide-and-conquer approach.
Example of where delaying digitization and doing analog preprocessing gives you a
huge win: The world’s lowest power programmable analog bionic ear processor. The
power is so low that it can run a rechargeable 100mAh battery for 30 years, and will be
lower in power than an A-D-then-DSP implementation even at the end of Moore’s law.
This is because its power is lower than that of a low power microphone and A/D alone.
The processor is useful for ultra-low-power speech-recognition front ends as well.
Other Examples: Neuron-inspired spike-based A-to-D converter with hybrid
signal representation and Analog Memory