ACM 97 Semiconductors Carver Mead Gordon and Betty Moore Professor of Engineering and Applied...

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ACM 97 Semiconductors Semiconductors Carver Mead Carver Mead Gordon and Betty Moore Professor of Engineering and Applied Science, California Institute of Technology

Transcript of ACM 97 Semiconductors Carver Mead Gordon and Betty Moore Professor of Engineering and Applied...

Page 1: ACM 97 Semiconductors Carver Mead Gordon and Betty Moore Professor of Engineering and Applied Science, California Institute of Technology.

ACM 97

SemiconductorsSemiconductors

Carver MeadCarver MeadGordon and Betty Moore Professor of Engineering and Applied Science, California Institute of Technology

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ACM 97ACM 97THE NEXT 50 YEARS OF

COMPUTING

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ACM 97ACM 97

Copyright Copyright 1997 ACM, Association for Computing 1997 ACM, Association for Computing

The files on this disk or server have been provided by ACM. Copyright and all rights therein are maintained The files on this disk or server have been provided by ACM. Copyright and all rights therein are maintained by ACM. It is understood that all persons copying this information will adhere to the terms and constraints by ACM. It is understood that all persons copying this information will adhere to the terms and constraints invoked by ACM’s copyright. These works may not be reposted without the explicit permission of ACM. invoked by ACM’s copyright. These works may not be reposted without the explicit permission of ACM. Reuse and/or reposting for noncommercial classroom use is permitted. Questions regarding usage rights Reuse and/or reposting for noncommercial classroom use is permitted. Questions regarding usage rights and permissions may be addressed to: [email protected] permissions may be addressed to: [email protected]

THE NEXT 50 YEARS OF COMPUTINGTHE NEXT 50 YEARS OF COMPUTING

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ACM 97CARVER MEADCARVER MEAD

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ComputationComputation

Church-Turing Thesis: Any Church-Turing Thesis: Any computable computable functionfunction can be computed on a Turing can be computed on a Turing machine.machine.

Mead's Thesis: The Class of Computable Mead's Thesis: The Class of Computable Functions is defined by Algorithms that Functions is defined by Algorithms that Execute Efficiently on Commercial Execute Efficiently on Commercial Machines.Machines.

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ComputationComputation

Complexity Theory:Complexity Theory:Time: The number ofTime: The number of steps required to steps required to execute a program execute a program Space: The number of memory locations Space: The number of memory locations required by a program.required by a program.

Assumption:Assumption:The computation done by different machines The computation done by different machines (per step or per unit hardware complexity) (per step or per unit hardware complexity) differs by at most a polynomial factor.differs by at most a polynomial factor.

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What ifWhat if

We Could Build a Computing Structure We Could Build a Computing Structure

With a Computational Capability With a Computational Capability

That Increased Exponentially with Its SizeThat Increased Exponentially with Its Size

It Would Completely Change the Game!It Would Completely Change the Game! Candidate Structures:Candidate Structures:

– Ultra-Parallel Digital VLSI Structures Ultra-Parallel Digital VLSI Structures – Neural Computing Structures Neural Computing Structures – Quantum Computing StructuresQuantum Computing Structures

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Ultra-Parallel Digital VLSI Ultra-Parallel Digital VLSI StructuresStructures

The Good News:The Good News:Remarkable Speedup Can De Achieved Remarkable Speedup Can De Achieved for Many Functionsfor Many Functions

The Bad News:The Bad News:Speedup is No More than PolynomialSpeedup is No More than Polynomial

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What is Going On ?What is Going On ?

Digital Systems:Digital Systems:Represent information by a finite set of Discrete Represent information by a finite set of Discrete SymbolsSymbolsDigital Representation Permits Information to beDigital Representation Permits Information to be– Transmitted through Space Transmitted through Space – Stored through Time without LossStored through Time without Loss

Signal Representing the Symbol is Restored to the Signal Representing the Symbol is Restored to the Nearest Symbol by a Contractive MappingNearest Symbol by a Contractive Mapping

Precision of the Representation is exponential in Precision of the Representation is exponential in the Number of Symbols per Valuethe Number of Symbols per Value

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Digital System LimitationsDigital System Limitations

Have No Natural Representation for Have No Natural Representation for TimeTime

All Continuous Variables Must Be All Continuous Variables Must Be Represented by Finite Strings of Represented by Finite Strings of Discrete SymbolsDiscrete Symbols

Process Information in Discrete ChunksProcess Information in Discrete Chunks Have No Notion of Locality or ContinuityHave No Notion of Locality or Continuity

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Digital System LimitationsDigital System Limitations When Used to Simulate Continuous Non-Linear When Used to Simulate Continuous Non-Linear

Systems Systems No General Criterion is Known for Numerical StabilityNo General Criterion is Known for Numerical Stability

Most Computations Dominated by Aliasing ArtifactsMost Computations Dominated by Aliasing Artifacts Alternative Hypotheses Have Only Discrete Alternative Hypotheses Have Only Discrete

RepresentationRepresentation Exponential Alternatives Require Exponential Exponential Alternatives Require Exponential

ResourcesResources Quantizes After Every Quantizes After Every (Very Simple)(Very Simple) Operation Operation

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Neural ComputationNeural Computation Signals Transmitted over Distance:Signals Transmitted over Distance: Represented as Events Represented as Events

– Discrete in Amplitude Discrete in Amplitude – Discrete in TimeDiscrete in Time– Continuous Arrival Time VariableContinuous Arrival Time Variable

Local Signals:Local Signals:

Electrical PotentialElectrical Potential Continuous in AmplitudeContinuous in Amplitude

Chemical Concentration Continuous in TimeChemical Concentration Continuous in Time Information is encoded in Spatio-Temporal Structure of Information is encoded in Spatio-Temporal Structure of

SignalsSignals

}-{

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Neural ComputationNeural Computation Signals are Decoded in Highly Branched Dendrite StructureSignals are Decoded in Highly Branched Dendrite Structure

– Arrival Time Aligned by Propagation DelayArrival Time Aligned by Propagation Delay– Temporal Integrity MaintainedTemporal Integrity Maintained– Continuous AmplitudeContinuous Amplitude– Active AmplificationActive Amplification– NonLinear InteractionNonLinear Interaction– Adaptive Control keeps Structure StableAdaptive Control keeps Structure Stable– Positive and Negative FeedbackPositive and Negative Feedback

Keeps Many Combinatorial Possibilities Active in Same Keeps Many Combinatorial Possibilities Active in Same Structure Avoids Pre-QuantizationStructure Avoids Pre-Quantization

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Neuromorphic VLSI SystemsNeuromorphic VLSI Systems Silicon and Wetware Have Similar PhysicsSilicon and Wetware Have Similar Physics Basic Continuous RepresentationBasic Continuous Representation

– Continuous TimeContinuous Time– Limited PrecisionLimited Precision– Many Operations Come Free From Physics Many Operations Come Free From Physics

Interconnection Limits ComplexityInterconnection Limits Complexity– Energy is PreciousEnergy is Precious– Stability is a major ConcernStability is a major Concern

Natural World as Source of InformationNatural World as Source of Information– Real-Time SystemsReal-Time Systems– Adaptation to EnvironmentAdaptation to Environment– Learning vs programmingLearning vs programming– Time Evolution as Source of LearningTime Evolution as Source of Learning

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Many Functional Sub-Systems Have Many Functional Sub-Systems Have Been BuiltBeen Built

Vision SystemsVision Systems– RetinasRetinas– Motion PerceptionMotion Perception– Stereo MatchingStereo Matching

Auditory SystemsAuditory Systems– CochleasCochleas– Auditory Feature ExtractionAuditory Feature Extraction– Stereo LocalizationStereo Localization

In Situ LearningIn Situ Learning– Floating Silicon GatesFloating Silicon Gates– Autonomous On-Chip OperationAutonomous On-Chip Operation– Weight Modification when In UseWeight Modification when In Use

Has Not Yet All Come TogetherHas Not Yet All Come Together

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Quantum ComputationQuantum Computation Information Encoded in Information Encoded in Spatio- Temporal Structure Spatio- Temporal Structure of of

Many-Body Wave FunctionMany-Body Wave Function Phase Space for Coupled Many-Body System is Phase Space for Coupled Many-Body System is

Cartesian Product of Individual Phase SpacesCartesian Product of Individual Phase Spaces– Greatly Enlarged DimensionalityGreatly Enlarged Dimensionality

Time Evolution of Coupled Many-Body SystemTime Evolution of Coupled Many-Body SystemExplores Volume in Phase Space that isExplores Volume in Phase Space that isExponential inExponential in the Number of Dimensionsthe Number of Dimensions– All in ParallelAll in Parallel

Physical Size of System isPhysical Size of System isLinear Linear in the Number of Dimensionsin the Number of Dimensions

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Quantum ComputationQuantum Computation Theory: New Model of ComputationTheory: New Model of Computation Experiment:Experiment:

– Many Delightful Model SystemsMany Delightful Model Systems– Working Understanding of Quantum MechanicsWorking Understanding of Quantum Mechanics

Problems: Unwanted Coupling to rest of Problems: Unwanted Coupling to rest of UniverseUniverse– DePhasing of the Wave FunctionDePhasing of the Wave Function– Limits Computational PossibilitiesLimits Computational Possibilities

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ACM 97CARVER MEADCARVER MEAD

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James BurkeJames Burke

Master of CeremoniesMaster of Ceremonies

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