research to reality – the darpa perspective · approved for public release, distribution...
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Research to Reality – The DARPA Perspective
Dr. Todd Hylton
Program Manager
Defense Advanced Research Projects Agency
Northeastern University, Boston, MA
October 19, 2010
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What makes DARPA unique…
Formed in 1958 to PREVENT and CREATE strategic surprise
As the DoD’s innovation engine, weare committed to the boldest, creative leaps…
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Capabilities, mission focused
Finite duration projects
Diverse Performers
Multi-disciplinary approach…from basic research to system engineering
Creative leaps require…
Bold, best-in-class technical experts and knowledgeable, lean, adaptable support staff…
…who recognize opportunities and are empowered to act rapidly…
…and are unafraid to challenge conventional viewpoints or methods.
Protect the Fragile/FleetingBest Jumpers Challenge One’s
World View
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Regina E. Dugan, DirectorKaigham J. Gabriel, Deputy Director
Adaptive Execution
Dick UrbanRyan Patterson
Strategic Technology
Don WoodburyLarry Stotts, Brent Appleby
Tactical Technology
Dave Neyland Roger Hall
Information Innovation
Dan KaufmanMark Luettgen, Norman Whitaker
Defense Sciences
Leo ChristodoulouJon Mogford
Microsystems Technology
Greg KovacsMark Rosker
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DARPA Technical Offices
DARPA's Defense Sciences Office (DSO) Pursues and Exploits Fundamental Science and Innovation for
National Security
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Characteristics of a DARPA Program
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• Revolutionary change (not extensions or incremental gains)
• Empowered by ideas
• Project centric ̶ not investigator centric
• Creates opportunities, “encourages” teams
• Flexible, rapid review and contracting
• Actively managed by the Program Manager
• Driven by technical goals and milestones
Example Program - SyNAPSE
7
SyNAPSE Motivation and Objective
von Neumann Machines
Neuromorphic Machines
Machine Complexity
e.g. Gates;Memory;Neurons;Synapses
Power;Size
[log]
• Human level performance• Dawn of a new age
Dawn of a new paradigm
“simple” “complex”
Environmental Complexitye.g. Input Combinatorics
[log]
Program Objective
What is the difference?
Problem
• As compared to biological
systems, today’s
computing machines are
less efficient by a factor
of a million to a billion in
complex environments.
GOAL
Create biologically-inspired, neuromorphic electronic circuits with small physical dimensions, low power consumption, and the ability to understand and respond to their environments.
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SyNAPSE Concept
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5X108
transistors/ cm2
@ 500 transistors/ neuron
~1010
intersections/cm2
@ 100 nm pitch
~1010
synapses /cm2
Multi-Gbit/sec digital comms
~106
Neurons /cm2
~5X108
long range axons @ 1 Hz
NeuromorphicElectronic System
CROSSBAR JUNCTION
CMOS SUBSTRATE
LAMINAR CIRCUIT
HIGH SPEED BUS
~104
Neurons / cortical column
Human Brain
SyNAPSE Program Approach
Hardware
Architecture
Simulation
Environment
CMOS based electronic neuronal
circuitry
Neuro-anatomically inspired electronic architecture e.g. spiking neural network with activity dependent
synaptic plasticity and network connectivity.
Circuit design and large-scale simulation for system validation
and functional testing.
Virtual, scalable environment to train, test, evaluate and benchmark electronic neural systems ability to
sense, learn, adapt, and respond
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SyNAPSE Program Plan
Phase 1 Phase 2 Phase 3 Phase 4
Emul
atio
n&
Sim
ulat
ion
Simulate Large Neural Subsystem Dynamics
~106 neuron level Benchmark
Har
dwar
e
Component Synapse Development
Process and Component Circuit Development
~106 neuron single chip implementation
Arch
itect
ure
& T
ools
Microcircuit Architecture Development
106 Neuron Design for Simulation and Hardware Layout
~108 neuronmulti-chip robot
108 neuron design for simulation and hardware layout
~108 neuron level Benchmark
“Human” level Design (~1010 neuron)
Build Expand & Refine Expand & Sustain SustainEnvi
ronm
ent
Comprehensive Design Capability
Phase 0
CMOS Process Integration
system I/O
environment
high-speed global communication system
interface
dire
ct
prog
ram
min
g
interface interface
System Level Architecture Development
11
SyNAPSE Perspective
~1.5 yr
IC Transistor µProcessor Algorithmicmachines
60 + years
NeuralMicrocircuit
Synapse Neural “Fabric” Intelligentmachines
• Increased scalability• Defect tolerant• Self-adapting, learning • Path to biologically
competit ive intelligence0.75 yr
DARPA SyNAPSE
PE: XXX – Front Office to Fill InXXX – Front Office to Fill In
~1.5 yr ~1.5 yr ~1.5 yr
phase 0 phase 1 phase 2 phase 3 phase 4
today
• End of scaling• Defect intolerant• Software limited• No path to biologically
competit ive intelligence
Doing business with DARPA…
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• Become familiar with the challenges and opportunities of National
Security.
• Approach a program manager; they are the key to working with DARPA.
• Put your ideas in writing. Draft a white paper.
• Look for Requests for Proposals (RFPs) and Broad Agency
Announcement (BAAs) solicitations at www.darpa.mil ,
www.darpa.mil/sbir , or www.fedbizopps.gov .
• Think boldly. Embrace risk.
Please Keep in Mind
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• DARPA programs start quickly and END. Just because we have an
on-going program in a given domain does not imply we will continue
to invest in that area.
• We are looking for revolutionary ideas that provide new capability
• “Old problems” CAN be revisited if there is a compelling reason, e.g.,
if a technological breakthroughs or great ideas have emerged
• We are looking for focus, commitment and dogged persistence
What would you attempt to do
if you knew you could not fail?
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www.darpa.mil
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