a tribute to a. richard newton - university of california...
TRANSCRIPT
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A Tribute to A. Richard Newton
Rich was extremely dynamic and energetic, entrepreneurial, and, most of all, a visionary ywithout bounds and a continuing source of thought-provoking ideas.
inviting all of us to dismiss…inviting all of us to dismiss borders and explore the
unexplored.
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Acknowledgements
The contributions of the following people to this t ti tl i t dpresentation are greatly appreciated:
Adam Arkin, Hugo De Man, Luca de Nardis, Simone Gambini Jay Keasling Steve LevitanSimone Gambini, Jay Keasling, Steve Levitan, Subhasish Mitra, Clark Nguyen, Korneel Rabaey, Jaijeet Roychowdhury, Alberto y, j y y,Sangiovanni-Vincentelli, Ellen Sentovich, Stan Williams, Jacob White, the sponsors of the GSRC and the FCRP program, DARPA, NSF, and … Richard Newton
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Outline
Beyond MicroelectronicsBeyond MicroelectronicsKeys to Microelectronics’ SuccessSuccess in the “BeyondSuccess in the Beyond Microelectronics” ArenaTh R FlThe Reverse FlowReflections
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Microelectronics in the 40’s and 50’s
First IC, Texas Instruments, 1958
Junction transistorJunction transistor, AT&T, 1950
Point-contact transistor, AT&T, 1947
The Physics Era:Search for workable devices building substrates and manufacturing strategiesSearch for workable devices, building substrates, and manufacturing strategies
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Leading to Astonishing Results TodayScaling enabled integration of complexScaling enabled integration of complex systems with hundreds of millions of devices on a single die
SUN Niagara-2ISSCC 07, 500M trans.
IBM Power-6ISSCC 07, 700M trans.SSCC 0 , 00 t a s
Intel KEROM dual coreISSCC 07, 290M trans.
IBM/Sony Cell ISSCC 05, 235M trans.
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The Waning Days of Moore’s Law
Limitations imposed by physicsLimitations imposed by economicsp y
may ultimately end its long run (22 nm – 16 nm – 12 nm – …?)or may not …
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Beyond MicroelectronicsExciting times again Researchers intensely exploringExciting times again … Researchers intensely exploring broad range of novel components (nano)
Molecular switchesMolecular switchesJ. Heath, Caltech
MEMS disc resonatorC. Nguyen, UCB Polymer ultra-thin FET
Subramanian, UCB
Nano-optics
Plenty of others….
CNT FETsWong, Dai, Stanford
The Physics, Chemists, and Material Scientists Era:Search for workable devices, building substrates, and manufacturing strategies
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Beyond MicroelectronicsExciting times again Researchers intensely exploringExciting times again … Researchers intensely exploring broad range of novel components (bio)
Enzymes
Plenty of others….
Proteins
The Biologists Era:
DNA strandsE. coli
The Biologists Era:Search for workable devices , building substrates, and manufacturing strategies
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The Potential Is Huge
Advanced sensorsDisplay and interface technologiesUltra high-speed interconnectHealth monitoringDrug creation and deliveryg yCreation of new fabricsRemoval of pollutantsRemoval of pollutantsEnergy generation….
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Example: Fighting MalariaNatural artemisin cost: $1/doseNatural artemisin cost: $1/doseNeed: 700 ton / year
1-3 million people die every year from malaria300-500 million people infected
as a cure
Microbial synthesis of Artemisinin
as a cure
Reduces costby factor 10!
Courtesy: J. Keasling, UCB
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Example: Energy ProductionMicrobial Fuel Cells
GeobacterGeobacterSheanellaPseudomonas Brevibacillus
EnterococcusClostridiumClostridium
Insoluble electron acceptor
[REF: P. Aelterman, K. Rabaey et al., Environ. Sci. Technol 2006]
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Keys to Microelectronics SuccessMoving from the lab to a (long-term) profitable production line! What it takes:
A scalable manufacturing processA scalable manufacturing processA scalable design methodologyA crisp computational model
In other words … hard core engineering
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Keys to Microelectronics SuccessA l bl f t iA scalable manufacturing process
First Planar ICFairchild, 1961.
Planar transistor
12” wafer1990s.
Planar transistorFairchild, 1959.
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Keys to Microelectronics SuccessA li ti
Disciplined “platform-
Application
Kernels/BenchmarksProgramming Model:Models/Estimators
Architecture Platform
based” design methodology
Cl b t tiMi hit t ( )
Architecture(s)
Clear abstractionsStandardized interfacesConstrained design
Microarchitecture(s)
Functional Blocks,InterconnectCycle-speed, power, area
Circuits Platform
Constrained design spaceComposition rulesB d il bilit fManufacturing Interface
Circuit Fabric(s)S SV V SG
S G
SSV
V
SS SSVV VV SSGG
Broad availability of intellectual property
Manufacturing Interface
Basic device & interconnectstructures
Delay, variation,SPICE models
Silicon Implementation [Courtesy: R. Newton,A. Sangiovanni-Vincentelli]
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Invariable Point: Circuits PlatformBSIM5, 2006
M1 nout nin 0 0 nmos W=510n L=100n
Interfaces remain fixed – while underlying models evolve
Schihman-Hodges, 1968Other Invariable Points:Logic, Register Transfer, MicroarchitectureInterfaces remain fixed while underlying models evolve
Scalable design rules, Mead-Conway,1980.
GDS-IIContext-aware design rules,
2006.
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Crisp Computational Models
An abstract machine or programming language is called Turing complete or Turing equivalent, if it has a computational power equivalent to (i.e.,
bl f l i ) i lifi d d lcapable of emulating) a simplified model of a programmable computer known as the universal Turing machine. Being equivalent to the universal Turingequivalent to the universal Turing machine essentially means being able to perform any computational task – though it does not mean being able to performit does not mean being able to perform such tasks efficiently, quickly, or easily.
Allan Turing
[Ref: Definition from Wikipedia, the free encyclopedia]
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Can this Be Repeated in the “Beyond Microelectronics” Era?
Going beyond the labGoing beyond the lab …
F th l t tFor the complete story:http://www.nature.com/nature/comics/syntheticbiologycomic/index.html
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Interlude - Some TerminologyA gene is a segment of nucleic acid that contains the information necessary to produce a functional RNA product in a controlled manner. pRNA acts as a messenger between DNA and the protein synthesis complexesProteins are large organic compounds made of Myoglobin proteinMyoglobinMyoglobin proteinProteins are large organic compounds made of amino acids arranged in a linear chain and joined together by peptide bonds …. The sequence of amino acids in a protein is defined by a gene and p y gencoded in the genetic code.Enzymes: proteins that catalyze (i.e. accelerate) chemical reactions.
TIM enzyme
E(scherichia). coli: is one of the main species of bacteria living in the lower intestines of mammals.
[Ref: All definitions from Wikipedia, the free encyclopedia]E. coliE. coli
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Building a Biological Oscillator (Bacterial Blinker)(Bacterial Blinker)
Select a circuit architecture
A Biological
λ-cI protein
A Biological Inverter
[Ref: Elowitz & Leibler. 2000. Nature 403:335-8] [Courtesy Jay Keasling, UCB]
λ cI gene
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Building a Biological Oscillator (Bacterial Blinker)(Bacterial Blinker)
Composition rulesrules
[Ref: Elowitz & Leibler. 2000. Nature 403:335-8]
Plasmid DNA string (containing 3 genes λ cI, TetR, laCI)
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Building a Biological Oscillator (Bacterial Blinker)(Bacterial Blinker)
E. coli as the chassischassis (substrate)
[Ref: Elowitz & Leibler. 2000. Nature 403:335-8]
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Realizing the Blinker
UT Austin TeamiGEM 2004 Competition
Using fluorescence to display th t t
[Elowitz & Leibler. 2000. Nature 403:335-8]
the output
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Pioneering Synthetic Biology
Moving from ad-hoc to structured design
[Reference: Scientific American, June 2006]
Moving from ad hoc to structured design
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Engineering Tomorrow’s Designs
Th ti f l bi l i l f ti d t l
Synthetic Biology
The creation of novel biological functions and tools by modifying or integrating well-characterized biological components into higher-order systemsbiological components into higher order systems using mathematical modeling to direct the construction towards the desired end product.p“Building life from the ground up” (Jay Keasling, UCB)Keynote presentation, World Congress on Industrial Biotechnology and Bioprocessing March 2007and Bioprocessing, March 2007.
Development of foundational technologies• tools for hiding information and managing complexity
t th t b d i bi ti li bl• core components that can be used in combination reliably
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Microelectronics and Synthetic Biology
A. Richard Newton2nd International Conference on Synthetic Biology2nd International Conference on Synthetic BiologyBerkeley, May 2006.
[Courtesy: Berkeley Media Services]
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Engineering Tomorrows DesignsScalable Manufacturing MethodologiesScalable Manufacturing Methodologies
Need for high-yield high-throughput DNA synthesis process• N t ( l ) 500 5000 b / ith t f 10 9• Nature (polymerase): 500-5000 bases/sec with error rates of 10-9• Typical bio-synthesis process: 0.003 bases/sec with 10-2 error rate!Micro-array synthesis: parallel generation of multiple bases (oligos) –
f fusing technology remeniscent of semiconductor manufacturing
1 million “dots”/cm2dots /cm2
[Images courtesy of Affymetrix]
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Engineering Tomorrows DesignsScalable Manufacturing MethodologiesThe law of exponentials …
Cost for DNA sequencing and synthesis
Time to solve a protein structureTime to solve a protein structure
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Engineering Tomorrow’s Design
Learning from the Microelectronics ExperienceA Disciplined Platform Design Methodology forA Disciplined Platform Design Methodology for Synthetic Biology!?
Exploration of scalable computational fabricsExploration of scalable computational fabrics Deriving useful abstractions and interfacesDeveloping modeling and characterizationDeveloping modeling and characterization environmentsAutomating the synthesis processPopulating the design space
[2007 DAC - Session 36]
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A Platform-Methodology for Biology
[Courtesy: C. Myers, Univ. of Utah]
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Modeling (The Device Perspective)
Biomolecule interactions involve l t t tielectrostatics
Extremely complicated 3-D geometries and coupled PDEsFast solvers for analysis (Multipole, Multigrid, PFFT)
[Courtesy: J. White, MIT]Barnase-barstar receptor-ligand complex(Picture courtsey to J. Bardhan,M. Altman, B. Tidor)
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Modeling (The Circuit Perspective)Cell Inputs: Biochemicals Output: Cell Dies (or not)Cell Inputs: Biochemicals Output: Cell Dies (or not)
Common Model
( ) ( ( )) ( )dx t b
Reduction Problem
( ) ( ( )) ( )r r rdx t F x t b u t
dt= +
( ) ( )Tr ry t c x t=
Complicated network with Low order model thatComplicated network with thousands of parameters
Low order model that captures input-output
behavior [Courtesy: J. White, MIT]
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Analysis: BioSPICE[Courtesy: A. Arkin, UCB]
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Creating Shared LibrariesOpening the door for synthesisOpening the door for synthesis
http://parts.mit.edu/registry/index.php/Main_Page
[Courtesy: Drew Endy and Randy Rettberg, MIT]
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Creating Shared Libraries
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Some Hard QuestionsC l it f b t tiComplexity of abstractions
Main reason why analog VLSI never took offSame problems plague many of the “beyond micro-l t i ” l tfelectronics” platforms
○ E.g. Natural biological components have multiple interactions with other components in the cell
The opportunity: Advanced modeling model reductionThe opportunity: Advanced modeling, model reduction and analysis techniques developed over the history of microelectronics are very applicable and valuable in this domain as well
Lack of clear computational modelsStill being experimentally “discovered”Not obvious how many are needed – that is how many y y“applications domains”Is there such a thing as an equivalent to Turing completeness in life?
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Engineering Tomorrows DesignsSimilar Considerations Hold for the Nano-
Electronics and Nano-mechanics ArenasA Disciplined Platform-Based Design Methodology ─ The Process
Exploration of scalable computational fabrics Deriving useful abstractions and interfacesDeveloping modeling and characterization environmentsA t ti th th iAutomating the synthesis processPopulating the design space
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I ti S l bl M f t i St t iEngineering Tomorrows Designs
Innovative Scalable Manufacturing Strategies
ContactN I i tiNano-Imprinting
Self-assemblySurfaceMicro-machining
Self assembly
Example:16 kbit nanowire crossbar memory Manufactured using two successive imprint stepsMemory density: 3 5x1011 bits/cm2Memory density: 3.5x10 bits/cmCourtesy: S. Williams, HP Labs
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Scalable Computational FabricsExploring logical design using carbon-nanotube (CNT) FETs
Misalignment-immuneCNT 2 NAND G tChallenges:
Misaligned CNTs, metallic CNTs
CNT 2-NAND Gate
ANY number ofANY number of misaligned CNTs
Arbitrary logic functions~10% cell-level penaltyp y13X Energy-Delay-
Product benefit vs 32 nm CMOS
Undoped region guaranteescorrect operation
[Courtesy: S. Mitra and P. Wong, Stanford, DAC 07, Paper 51.3]
correct operation
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Modeling and CharacterizationExample: Mixed Technology Optical SystemsExample: Mixed-Technology Optical Systems
Empirical modelsExperimental data
Derived modelsParametric models extracted from or verified
Analytic modelsPhysics based
fitting by lower level tools
VddPin12
1416
(mW
)
2.5V
⎤⎡⎤⎡ 2 22
rW Pin
R0246810
0 5 10 15 20 25 30 35 40 45 50
Out
putP
ower
I t P ( W)
4V ⎥⎦
⎤⎢⎣
⎡−⎥
⎦
⎤⎢⎣
⎡=
)(2exp
)(),( 2
00 zW
rzW
WIzrI
)(1
)(0 sPs
ACR
RsV optic
f
f ⋅
⎟⎟⎠
⎞⎜⎜⎝
⎛+
=Input Power(mW)
[Courtesy: S. Levitan, University of Pittsburgh]
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Creating a Complete PlatformExample: Mixed Technology Optical SystemsExample: Mixed-Technology Optical Systems
250 μm
Chatoyant, Univ. of Pittsburgh
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The Reverse Flow of Ideas
Ideas emerging from the “Beyond Microelectronics” arenas may help to extend the span of Moore’s Law
Manufacturing processes that do not rely onManufacturing processes that do not rely on photolithographyComputational models that cope withComputational models that cope with variability and unreliability
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The Reverse Flow of IdeasBorrowing from emerging technologies to advance silicon manufacturing
Using self-assembly to create low-k interconnect
[Dail Tech[Daily Tech,May 4, 2007]
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The Reverse Flow of IdeasNanometer CMOS: A search for scalable and stackable abstractions that operate correctly
in the presence of large variationsat very low SNRin the presence of failures of devices and interconnectionsinterconnections
Bio inspired computing:Bio-inspired computing:Artificial neuron
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Example: State-of-the-art Synchronization
Precision Timing Element(Crystal)
Intel Itanium Clock distribution[ISSCC 05]
Clock phase and skew[P. Restle, IBM]
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Oscillators as Building BlocksOsc.Type
Unit Area(μXμ)
Unit Power@ 5 GHz
#/sq.mm Tot.Power
LC 300 300 >300 W 9 2 7 WLC 300x300 >300μW 9 2.7mW
MEMS 40x30 1μW 750 7.5mW
CMOS 3x3 100μW 90000 9WCMOS 3x3 100μW 90000 9W
Ring OscillatorLC OscillatorMEMS Disc Oscillator
[Courtesy: S. Gambini, UCB]
[Courtesy: C. Nguyen, UCB]
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Synchronization Inspired by Biological SystemsBiological Systems
Distributed synchronization usingDistributed synchronization using only local communications and without precision timing elements
EnergyEnergy distribution
[REF: Mirollo and Strogatz, 1990] time
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Synchronization in Distributed El t i S tElectronic Systems
[Courtesy: L. De Nardis, UCB/Roma]
Quick synchronization at low cost
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Concluding ReflectionsWith t h ll i iliWith nanometer challenges in silicon, microelectronics design is already escaping its borders
Nano and bio components will gradually andNano and bio components will gradually and transparently infiltrate the design space
True impact and success is only possible in the presence of truly scalable designin the presence of truly scalable design platforms – the true legacy of EDASynthetic biology a star in the making. A true opportunity for the daring … !true opportunity for the daring … !
“The future is BDA”, A. Richard NewtonIt takes broad understanding to move borders - Profound changes inborders - Profound changes in educational practices are needed to enable joint exploration of micro, nano, and bio opportunities.and bio opportunities.
Exciting times again …
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For your visionsFor your visionsFor your enthusiasmFor your enthusiasmFor your enthusiasmFor your enthusiasmFor your friendshipFor your friendshipThank you Rich!Thank you Rich!Thank you, Rich!Thank you, Rich!