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

3. ACM 97 THE NEXT 50 YEARS OF COMPUTING

4. ACM 97 Copyright  1997 ACM, Association for Computing 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 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 and permissions may be addressed to: permissions@acm.org THE NEXT 50 YEARS OF COMPUTING

5. ACM 97 CARVER MEAD

6. ACM 97

7. Computation Church-Turing Thesis: Any computable function can be computed on a Turing machine. Church-Turing Thesis: Any computable function can be computed on a Turing machine. Mead's Thesis: The Class of Computable Functions is defined by Algorithms that Execute Efficiently on Commercial Machines. Mead's Thesis: The Class of Computable Functions is defined by Algorithms that Execute Efficiently on Commercial Machines.

8. ACM 97 Computation Complexity Theory: Time: The number of steps required to execute a program Space: The number of memory locations required by a program. Complexity Theory: Time: The number of steps required to execute a program Space: The number of memory locations required by a program. Assumption: The computation done by different machines (per step or per unit hardware complexity) differs by at most a polynomial factor. Assumption: The computation done by different machines (per step or per unit hardware complexity) differs by at most a polynomial factor.

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16. What if We Could Build a Computing Structure With a Computational Capability That Increased Exponentially with Its Size We Could Build a Computing Structure With a Computational Capability That Increased Exponentially with Its Size It Would Completely Change the Game! Candidate Structures: Candidate Structures: – Ultra-Parallel Digital VLSI Structures – Neural Computing Structures – Quantum Computing Structures

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18. Ultra-Parallel Digital VLSI Structures The Good News: Remarkable Speedup Can De Achieved for Many Functions The Good News: Remarkable Speedup Can De Achieved for Many Functions The Bad News: Speedup is No More than Polynomial The Bad News: Speedup is No More than Polynomial

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20. What is Going On ? Digital Systems: Represent information by a finite set of Discrete Symbols Digital Representation Permits Information to be Digital Systems: Represent information by a finite set of Discrete Symbols Digital Representation Permits Information to be – Transmitted through Space – Stored through Time without Loss Signal Representing the Symbol is Restored to the Nearest Symbol by a Contractive Mapping Signal Representing the Symbol is Restored to the Nearest Symbol by a Contractive Mapping Precision of the Representation is exponential in the Number of Symbols per Value Precision of the Representation is exponential in the Number of Symbols per Value

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28. Digital System Limitations Have No Natural Representation for Time Have No Natural Representation for Time All Continuous Variables Must Be Represented by Finite Strings of Discrete Symbols All Continuous Variables Must Be Represented by Finite Strings of Discrete Symbols Process Information in Discrete Chunks Process Information in Discrete Chunks Have No Notion of Locality or Continuity Have No Notion of Locality or Continuity

29. ACM 97 Digital System Limitations When Used to Simulate Continuous Non-Linear Systems When Used to Simulate Continuous Non-Linear Systems No General Criterion is Known for Numerical Stability No General Criterion is Known for Numerical Stability  Most Computations Dominated by Aliasing Artifacts Alternative Hypotheses Have Only Discrete Representation Alternative Hypotheses Have Only Discrete Representation  Exponential Alternatives Require Exponential Resources Quantizes After Every (Very Simple) Operation Quantizes After Every (Very Simple) Operation

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33. Neural Computation Signals Transmitted over Distance: Signals Transmitted over Distance: Represented as Events Represented as Events – Discrete in Amplitude – Discrete in Time – Continuous Arrival Time Variable Local Signals: Local Signals: Electrical PotentialContinuous in Amplitude Chemical Concentration Continuous in Time Information is encoded in Spatio-Temporal Structure of Signals Information is encoded in Spatio-Temporal Structure of Signals }-{

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35. Neural Computation Signals are Decoded in Highly Branched Dendrite Structure Signals are Decoded in Highly Branched Dendrite Structure – Arrival Time Aligned by Propagation Delay – Temporal Integrity Maintained – Continuous Amplitude – Active Amplification – NonLinear Interaction – Adaptive Control keeps Structure Stable – Positive and Negative Feedback Keeps Many Combinatorial Possibilities Active in Same Structure Avoids Pre-Quantization Keeps Many Combinatorial Possibilities Active in Same Structure Avoids Pre-Quantization

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39. Neuromorphic VLSI Systems Silicon and Wetware Have Similar Physics Silicon and Wetware Have Similar Physics Basic Continuous Representation Basic Continuous Representation – Continuous Time – Limited Precision – Many Operations Come Free From Physics Interconnection Limits Complexity – Energy is Precious – Stability is a major Concern Natural World as Source of Information Natural World as Source of Information – Real-Time Systems – Adaptation to Environment – Learning vs programming – Time Evolution as Source of Learning

40. ACM 97 Many Functional Sub-Systems Have Been Built Vision Systems Vision Systems – Retinas – Motion Perception – Stereo Matching Auditory Systems Auditory Systems – Cochleas – Auditory Feature Extraction – Stereo Localization In Situ Learning In Situ Learning – Floating Silicon Gates – Autonomous On-Chip Operation – Weight Modification when In Use Has Not Yet All Come Together Has Not Yet All Come Together

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47. Quantum Computation Information Encoded in Spatio- Temporal Structure of Many-Body Wave Function Information Encoded in Spatio- Temporal Structure of Many-Body Wave Function Phase Space for Coupled Many-Body System is Cartesian Product of Individual Phase Spaces Phase Space for Coupled Many-Body System is Cartesian Product of Individual Phase Spaces – Greatly Enlarged Dimensionality Time Evolution of Coupled Many-Body System Explores Volume in Phase Space that is Exponential in the Number of Dimensions Time Evolution of Coupled Many-Body System Explores Volume in Phase Space that is Exponential in the Number of Dimensions – All in Parallel Physical Size of System is Linear in the Number of Dimensions Physical Size of System is Linear in the Number of Dimensions

48. ACM 97 Quantum Computation Theory: New Model of Computation Theory: New Model of Computation Experiment: Experiment: – Many Delightful Model Systems – Working Understanding of Quantum Mechanics Problems: Unwanted Coupling to rest of Universe Problems: Unwanted Coupling to rest of Universe – DePhasing of the Wave Function – Limits Computational Possibilities

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55. CARVER MEAD

56. ACM 97 James Burke Master of Ceremonies

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