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Cognitive Computing via Synaptronics and Supercomputing

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Presentation Transcript
slide2

"The information that comes from deep in the evolutionary past we call genetics. The information passed along from hundreds of years ago we call culture. The information passed along from decades ago we call family, and the information offered months ago we call education. But it is all information that flows through us. The brain is adapted to the river of knowledge and exists only as a creature in that river. Our thoughts are profoundly molded by this long historic flow, and none of us exists, self-made, in isolation from it."

slide5

Mammalian-scale simulation in near real-time?

Computation

Memory

Communication

slide6

Cat

763 x 106

6.1 x 1012

Monkey

2 x 109

20 x 1012

Mouse

16 x 106

128 x 109

Rat

56 x 106

448 x 109

Human

22 x 109

220 x 1012

Almaden

BG/L

December, 2006

Watson

BG/L

April, 2007

WatsonShaheen

BG/P

March, 2009

LLNL Dawn

BG/P

May, 2009

BlueGene Meets Brain

N:

S:

New results for SC09

Latest simulations achieve unprecedented scale of

109 neurons and 1013 synapses

slide8

Novel non-von Neumann Architectures are necessary

Data from Todd Hylton

Brain can be realized in modern electronics

slide9

Turning Back the Clock

Digital, synchronous conventional, 5GHz(compare Power 6, 2008)

Digital, semi-synchronous, 5 MHz(compare IBM PC/8088, 1978)

Digital-Analog, asynchronous, clockless(compare the Brain)

Digital, asynchronous, 100 kHz(compare ENIAC, 1946)

Commandment:Do what is necessary, when it is necessary, and only that which is necessary.

slide10

Dharmendra S ModhaIBM Research – Almaden

Raghavendra SinghIBM Research – India

Network Architecture of the White Matter Pathways in the Macaque BrainPNAS (July 2010)

the connection model
The connection model
  • Cortex has evolved such that it is organized into areas with distinct structural and functional properties
      • Primary sensory areas
      • Association areas
      • Motor areas
  • The white matter (myelinated nerve cell) underneath the outer covering of gray matter (nerve cell bodies), interconnects different regions of the central nervous system and carries nerve impulses between neuron
  • Model each area as a node and each connection as an edge in a graph
    • Analysis and Visualization of the brain
      • Wire length minimization
      • Organizational model that suggest the flow of information from input of sensory signals to the eventual output by motor neurons
    • Use model to simulate dynamics in the simulator
slide12

CoCoMac: Connectivity data on the Macaque brain

Rolf Kotter, Klass Stephen, 2000

413 literature reports

7007 brain sites

8003 mapping details

2508 tracer injections

39748 connection details

slide13

AP84-TE

PG91b-IT

BP82-46

SP89a-46

FV91-V4

FV91-TF

RV99-TF

FV91-V1

RV99-CA1

BR98-CA1

FV91-TH

BR98-TH

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Divergent Nomenclature and Abundant Conflicts

slide34

C, C, C, C, C, and C

Complete Cortex, Thalamus, Basal Ganglia

Comprehensive Includes every study in CoCoMac

Consistent Every connection can be tracked back

Concise 6,877 areas to 383

Coherent Unified hierarchical parcellation

Colossal 3 times larger than previous network

wetware to software

slide36

Brain is small-world

SCC: 351 areas, 6,491 connections

slide41

Brain is small-world, Core is “tiny”-world!

Core contains only 32% of vertices yet 88% of all edges originate or terminate in the core

slide42

Core contains correlated-anti-correlated networksand may be a key to consciousness

Fox, Snyder, Vincent, Corbetta, Van Essen, and Raichle, 2005

slide44

Rent’s Rule

  • Rent\'s rule pertains to the organization of computing logic, specifically the relationship between the number of external signal connections (C) to a logic block with the number of logic gates (N) in the logic block
  • E.F. Rent observed a power-law relationship in the 1960’s - the law has been shown to hold true for small circuits upto mainframe computers

0 ≤ p≤1 is the Rent parameter and k is the Rent coefficient.

  • Intrinsically it’s a surface area (wire) to volume (number of nodes) relationship
    • Represents a cost-efficient solution to the challenge of embedding a high dimensional functional interconnect topology in a relatively low dimensional physical space with economical wiring costs
  • Circuits with greater logical capacity have higher values of Rent parameter
    • Microprocessor (0.45), Gates Arrays (0.5), High speed Computers (0.63)
  • For 2D layouts p> 0.5 implies that wires must grow longer as circuit size increases; global connections dominate over local connections for large p
    • The relative contribution of wiring to layout area will grow with the size of circuit to allow space for a greater number of wires to pass between adjacent nodes, increasing the node-to-node spacing
  • Allometric scaling
    • Gray (physical) to white (logical) matter scaling - Zhang & Sejnowski
slide45

Rent’s Rule

  • High value of p
    • Topological dimensionality of network greater than 3, i.e., greater than the dimensionality of the Euclidean space in which the network is embedded
    • Communication is a significant factor of power and space
    • Tradeoff between wiring costs and greater logical capacity.
      • Rewiring the network so as to reduce its topological dimension results in loss of functional modularity
slide48

Owing both to limitations in hardware and architecture, these (convential) machines are of limited utility in complex, real-world environments, which demand an intelligence that has not yet been captured in an algorithmic-computational paradigm. As compared to biological systems for example, today’s programmable machines are less efficient by a factor of one million to one billion in complex, real-world environments. The SyNAPSE program seeks to break the programmable machine paradigm and define a new path forward for creating useful, intelligent machines.

The vision for the anticipated DARPA SyNAPSE program is the enabling of electronic neuromorphic machine technology that is scalable to biological levels. Programmable machines are limited not only by their computational capacity, but also by an architecture requiring (human-derived) algorithms to both describe and process information from their environment. In contrast, biological neural systems (e.g., brains) autonomously process information in complex environments by automatically learning relevant and probabilistically stable features and associations. Since real world systems are always many body problems with infinite combinatorial complexity, neuromorphic electronic machines would be preferable in a host of applications—but useful and practical implementations do not yet exist.

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