cognitive computing via synaptronics and supercomputing

© 2008 IBM Corporation

Cognitive Computing via Synaptronics and Supercomputing


"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."


Inflection Point 1: Neuroscience has matured

1414 pages


Inflection Point 2: Supercomputing meets Brain


Memory Computation

Communication

Mammalian-scale simulation in near real-time?


LLNL Dawn

BG/P

May, 2009

Human

22 x 109

220 x 1012

Rat

56 x 106

448 x 109

Mouse

16 x 106

128 x 109

N:

S:

Monkey

2 x 109

20 x 1012

Cat

763 x 106

6.1 x 1012

Almaden

BG/L

December, 2006

Watson

BG/L

April, 2007

WatsonShaheen

BG/P

March, 2009

BlueGene Meets Brain

Latest simulations achieve unprecedented scale of

109 neurons and 1013 synapses

New results for SC09


Inflection Point 3: Nanotechnology meets Brain


Rat Human

Power 50 mW 20 W

Space 6 cm2 2,400 cm2

Brain Neuromorphic Electronics

~1010 synapses/cm2 1010 intersection/cm2 in 100 nm crossbar

~106 Neurons/cm2 ~5x108 transistors/cm2 in state of the art CMOS

~5 x 108 long range axons @ ~1 Hz

~30 Gbit/sec multiplexed digital addressing

Novel non-von Neumann Architectures are necessary

Brain can be realized in modern electronics

Data from Todd Hylton


Turning Back the Clock

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

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

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

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

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


Network Architecture of the White Matter Pathways in the Macaque Brain

PNAS (July 2010)

Dharmendra S ModhaIBM Research – Almaden

Raghavendra Singh IBM Research – India


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


CoCoMac: Connectivity data on the Macaque brain

413 literature reports

7007 brain sites

8003 mapping details

2508 tracer injections

39748 connection details

Rolf Kotter, Klass Stephen, 2000


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Divergent Nomenclature and Abundant Conflicts


Species Study Areas Connections

Monkey Felleman, Van Essen, 91 32 305

Young, 93 70 700

Kaiser, Hilgetag, 06 95 2,402

Cat Scannell et al., 95 65 1,139

Scannell et al., 99 95 1,500

Notable Collations


Species Study Areas Connections

Monkey Felleman, Van Essen, 91 32 305

Young, 93 70 700

Kaiser, Hilgetag, 06 95 2,402

Cat Scannell et al., 95 65 1,139

Scannell et al., 99 95 1,500

Monkey This Collection 383 6,602

Notable Collations


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

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

wetware to software


Nodes 383

Edges 6,602

Density 4.5% of possible connections exist

Reciprocity 42%

SCC 351 areas, 6,491 edges

Aggregate Statistics


Brain is small-world

Brain Random(100 trials)

Diameter 6 3.93

Characteristic Path Lengh 2.62 2.30

Average Clustering Coefficient 0.33 0.0528

Reciprocity 42% 5.34%

SCC: 351 areas, 6,491 connections


“Organized Complexity” – Weaver, 1948


Degree Distribution Consistent with Exponential


Prefrontal Cortex is Topologically Central


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

Brain Core

Diameter 6 4

Characteristic Path Lengh 2.62 1.95

Average Clustering Coefficient 0.33 0.39

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


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

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


Inter-chip Connectivity


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

€

C = kN p

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

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


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


Nicolaus Steno, 1669

“white matter is nature’s finest masterpiece”


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.

cognitive computing via synaptronics and supercomputing

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