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sarpeshkar banbury Mb Computational Neuroscience

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									HYBRID COMPUTATION WITH SPIKES

          Rahul Sarpeshkar
   Robert J. Shillman Associate Professor
                    MIT
    Electrical Engineering and Computer Science




Supported by the Swartz Foundation and NSF        Banbury Sejnowski talk
                                                  5/18/04
                              SUMMARY
1.   I show how analog processing instead of traditional A-D-then-DSP processing can
     result in huge wins in energy efficiency, for example, in a bionic ear processor for
     the deaf that is soon to go commercial and that is likely to be unbeatable even at the
     end of Moore‟s law.
2.   Analog is more efficient than digital at low precision and vice versa. Hybrid
     computation can be more efficient than either because it is based on a better tradeoff
     between robustness and efficiency in computational systems compared with the
     analog and digital extremes.
3.   Spike count is digital, interspike intervals are analog, so spikes are natural for hybrid
     computing. I show how spikes can be used to create „carries‟ and create a distributed
     representation of a real number.
4.   I describe the architecture of an HSM, a Hybrid State Machine built with spikes,
     which generalizes the notion of Finite State Machines (FSMs) in digital computation
     to the hybrid domain.
5.   One of these HSMs, a two-spiking-neuron HSM, is among the world‟s most energy-
     efficient A/D converters and is the first time-based converter that achieves linear
     scaling in conversion time with bit precision instead of exponential. It works by
     converting spike-time information to spike-count information in a recursive fashion
     with an underlying clock providing synchrony.
6.   Every spike matters in these computations but there can be some redundancy for
     error correction.
7.   A synthetic engineering approach that exploits the analog and digital aspects of
     spikes for efficient computation may provide new ideas for how spikes could be used
     in neurobiology and complement traditional analytic approaches.
                    The charge from the electrode
                    stimulation pulses is conducted to the
            1       spiral ganglion cell and activation
        4       5   occurs.
                                               7
                                               6



    2



                THE BIONIC EAR
3
  ULTRA-LOW-POWER ANALOG PROCESSOR FOR
BIONIC EARS (COCHLEAR IMPLANTS) AND SPEECH
                RECOGNITION
NOISE IN ANALOG DEVICES AND SYSTEMS
HOW MUCH ANALOG DO YOU DO BEFORE YOU GO DIGITAL?
Example: Is the number of input pulses greater than 211-1?
      “Analog” DSP:A Hybrid Multiplier




•   We let Q=I*T do the elementary multiplication
•   Kirhchoff‟s current law does addition
•   Spiking neuron circuits perform carries in ripple-carry fashion.
•   Precision can be adapted with speed
        THE HYBRID STATE MACHINE (HSM)
     FINITE STATE MACHINE                       HYBRID STATE MACHINE (HSM)




1.   “Spike” = Pulse or Digital Event.
2.   Each discrete state in the HSM is like a „behavior‟ in which a rapidly reconfigurable
     analog dynamical system changes its parameters or topology.
An HSM for Successive Approximation A/D Conversion
     SPIKING A-TO-D CONVERTER
1.    Among the world‟s most energy-efficient converters. The first time-
      based converter that achieves a linear scaling in conversion time
      with bit precision instead of exponential scaling.
2.    Underlying Clock provides synchrony for operation.
3.    Spike-time and spike-count (1 or 0) codes toggle back and forth
      between each neuron. Thus, count and time codes are
      simultaneously present.
4.    The count code (s) may be viewed as performing successively more
      precise digital signal restoration on the original analog input timing
      signal.
5.    Every spike matters in the computation.
6.    Can build similar HSMs for pattern recognition, learning, and
      analog memory.
                              SUMMARY
1.   I show how analog processing instead of traditional A-D-then-DSP processing can
     result in huge wins in energy efficiency, for example, in a bionic ear processor for
     the deaf that is soon to go commercial and that is likely to be unbeatable even at
     the end of Moore‟s law.
2.   Analog is more efficient than digital at low precision and vice versa. Hybrid
     computation can be more efficient than either because it is based on a better
     tradeoff between robustness and efficiency in computational systems compared
     with the analog and digital extremes.
3.   Spike count is digital, interspike intervals are analog, so spikes are natural for
     hybrid computing. I show how spikes can be used to create „carries‟ and create a
     distributed representation of a real number.
4.   I describe the architecture of an HSM, a Hybrid State Machine built with spikes,
     which generalizes the notion of Finite State Machines (FSMs) in digital
     computation to the hybrid domain.
5.   One of these HSMs, a two-spiking-neuron HSM, is among the world‟s most
     energy-efficient A/D converters and is the first time-based converter that achieves
     linear scaling in conversion time with bit precision instead of exponential scaling.
     It works by converting spike-time information to spike-count information in a
     recursive fashion with an underlying clock providing synchrony.
6.   Every spike matters but there can be some spike redundancy for error correction.
7.   A synthetic engineering approach that exploits the analog and digital aspects of
     spikes for efficient computation may provide new ideas for how spikes could be
     used in neurobiology and complement traditional analytic approaches.

								
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