http://nano.ece.duke.educhris dwyer, duke university dna-based spatial computing: toward...

http://nano.ece.duke.eduChris Dwyer, Duke University

DNA-based Spatial Computing:Toward Diffusion-limited Computation

Chris DwyerAssistant ProfessorDepartment of Electrical and Computer Engineering, Department of Computer Science

Spatial Computing Workshop / SASO, September 2009


Dwyer Lab Overview

[DNA] self-assembly is a technology that will enable next-generation materials, computers and systems

Self-organizing architectures

Metamaterials

Sensors

Grids: 160nm X 160nm

AAO templatingPrecise chemical patterning


Motivation – Performance

log Length (m)

log Cost ($/gate)

log Switching time (s)

DNA is here


Motivation – Cost! What infrastructure?

Fab. Cost*Year

~$1B2000

=$1.6B2002

=$2B2004

=$3B2006

…

*EE Times, †2004 U.S. Census, ‡ Yahoo! Finance

…

=$4B+?2009

… …

(32 nm)

(65 nm)

(90 nm)

(180 nm)

$1,170B

$ 260B Semiconductors

Drug Manufacturing

Specialized Chemical Mfg. $ 247B

$ 349BBiotechnology

Market Capitalization‡

Value of goods sold†

~$75B Semiconductors

~$400B (Non-petrol.) Chemical mfg.

$1,766B

+

Conven.

DNA self-assembly

Microelectronics


Motivation – New Domains

• Hybrid CMOS-nano Systems– Novel material to enhance existing CMOS

• New Systems– Novel materials to introduce computing to entirely

new domains

Intel 8088 HIV-1 budding from lymphocyte (CDC)


Outline

• Self-assembled Nanostructures– DNA

– Scaffolds

• Devices– Fluorescence Resonance Energy Pathways and Logic

• Self-assembled Systems– Fusing Logic and Sensors

– Diffusion-limited Computation

• Conclusions


DNA

• A DNA strand:– A linear array of bases (A, T, G, and C)– Directional (one end is distinct from the other)– In nature, the source of genetic information

• DNA will form a double helix:– When the bases on each strand (aligned “head-to-

toe”) are complementary: A with T, and G with C

– But only under certain “natural” environmental conditions (low) temperatures (Tm: sequence dependent) and in an ionic solution.


How to build with DNA

• Leverage DNA thermodynamics to control assembly

T


How to build with DNA

• Double helix (B-form) has well-known geometric properties:– 3.4 Å per base pitch along the helix– One complete turn between every 10th and 11th base

• Flexibility: the bonds along the sugar-phosphodiester backbone reptate– single stranded DNA has a strongly sequence

dependent persistence length (but, it’s small ~1nm)– double stranded DNA has a ~50nm persistence length


Outline


– Scaffolds




• Conclusions


DNA Scaffolds - Geometry

• The geometric properties of double strands can form specific, controlled self-assembled nanostructures:

T 3.4 Å


DNA Motifs

9 strands

. . .


Molecular

precision

scaffold

DNA Motifs

Manufacturing scale: >1015 grids/mL

60nm

Atomic Force Microscopy (AFM) images of assembled grids

60nm x 60nm DNA grid Protein-patterned DNA grid

Multiple DNA grids deposited on flat mica plane Size Scaling


How? Sequence Design

…CGGGTTA

TAACCG…

TAATCG…

TAAACG…

?

?

Major challenge:100,000s of CPU-hrsfor simple design “turns”.(100 nt strand 1060 combinations)

96-Arm System

-20

-15

-10

-5

0

5

10

15

20

25

0 5 10 15 20 25

Specific Tm

No

n-S

pec

ific

Tm

TextRandomThermo 1Thermo 2

1:1 diagonal

better


The DNA Foundry

• Modeled after the modern silicon foundry• Turn-key mfg. of precise nanostructures• Leverages economies-of-scale• Consolidated design services• Uniform interface(s) for foundry services• Leverage modularity and pipelining to

minimize mfg. latency

A Subsidiary of Parabon Computation,Inc.A Subsidiary of Parabon Computation,Inc.


Outline


– Scaffolds



– Self-organizing Computer Architectures

• Conclusions


Operational Overview

Input

01010

11001

00101

……..

Output11101

01010

10111

……...


D0+A0+hvD

AD

hvA

D0+A0

hv1

RET

D*+A0

D0+A* D0+A0+hvA

ADhv1

AD AD

hvD

ADRET

Resonance Energy Transfer

T


RNR

RT

X Z

RCross-Ex.

F < FT

F > FT

)1( TT

TE

R

RIF

(1)

(2)

(3)

X Z (energy migration)

CP

RET Circuit Theory

Other elements

RET Cascades

0N

EI

I0

I1

I2

0P

F

R1

R2

+

-

F0F=F0·R2/(R1+R2)

IE=F0/(R1+R2)


X

Y X + Y

OR

R

X

Y X Y

AND

•OR / AND primitives •No signal gain

•A solution: inverting pass gate

RET Circuit Theory

OUTIN

PASS


Toward the Inverting Pass Gate

hνIN

hνOUT

hνGATE

hνIN

GroundExcitedAbsEm

X‘1’

X

inverter

No output

260

6

6

01,

Rr

RrRT


Outline


– Scaffolds




• Conclusions


•2-input, single output

OG RR

T 10

PO

T 14

IN 1 488nm

IN 2 400nm

OUT 590nm

•Expect: energy from inputs IN1 and IN2 carries to same OUT

Input 1 : OG Ex 488nm Em 525nm

Input 2 : PO Ex 400nm Em 550nm

Acceptor : RR Ex 570nm Em 590nm

Sample : 25nM, 1cm path

Excitation : 488nm

Output : 500-700nm

Demonstrated RET Logic


Why: To Fuse Logic and Sensors

Ligand-receptor binding (by AFM)- RNA- proteins- etc.

R2

X

Y X Y R2/(R1+R2)R1

1. XY forms distinct “address”2. Output depends on R2/(R1+R2)

RL

RH

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

SA SB

A-/B- A+/B- A-/B+ A+/B+H/H L/H H/L L/L

XY XZ


Where to now?

• Historically, the development of a circuit technology takes decades– RET logic is to the “handful” of gates (LSI) stage…

• Given the alternatives, a new technology must have the potential to achieve fundamentally new capabilities

DNA self-assembled systems can compute in spaces where conventional technologies cannot.

Focus: biological environments


Computational Perspective

Early disease detection

Detect pathogen levels

(e.g. counting viruses)

Counting sensor events

Monitoring complex bio-scale processes

Detect sequences of micro-environment changes

Data-driven control sequences

Determine binding and dissociation constants

Accumulation of sensor values

Monitor proteins and cellular gene expression

FIR filters

High density gene chips

Maps of nanoscale data Block read and transfer

Biology/Lab Computer ScienceApplication


Example Application

Monitoring complex bio-scale processes

Detect sequences of micro-environment changes

Data-driven control sequences

Event1: wait until (sensed(A) is true)

Event2: wait until (sensed(B) is true and sensed(C) is true) Event3: wait until (sensed(D) is true and sensed(A) is false)

then set output(true)

Biology/Lab Computer ScienceApplication


New Domain: Diffusion-Limited Computation

• New requirements: diffuse, compute and sense in nanoscale volumes – E.g. operation within a cell (red blood cell diameter: 6-8 μm)

• Size requirement excludes current CMOS solutions– Large (tens of microns) silicon chips do not diffuse freely

• Need a new solution


Nanoscale Sensor Processors (nSP)

• Integrate molecular sensors and molecular digital logic

– sense – process – store– communicate

• Meet size and functionality requirements

Molecular Probes + Processing = Automation

SENSOR

MEMORY

PROC

COMM

ARRAY

SENSOR

MEMORY

PROC

COMM

ARRAY

molecular information


RET Logic Circuits on DNA Grid

• I/O and power: no routing necessary (global optical signals)• Expected switching time 2ns, dissipated power 0.4nW (FO1

pass-gate) [IEEE Micro 2008]

• Directly compatible with the wide range of available RET sensors• Sensing accomplished by disrupting RET with binding events • Technology for sensing and computing

a

c

d

a

d

b

b

g

g a

b

b

a

AB

C_OUTC_INc

ac C_IN

S

C_OUT

a

c

d

a

d

b

b

g

g a

b

b

a

AB

C_OUTC_INc

ac C_IN

S

C_OUT

a

c

b

b

c

g

S0 E

O0

b

O3

O1

O2a

g

dc

c

S1

ba

a

c

b

b

c

g

S0 E

O0

b

O3

O1

O2a

g

dc

c

S1

ba

c b

a

g

g a

c

d

b

R_S

D W_S

c

40x40nm 1-bit full adder

60x40nm decoder60x40nm memory cell

I

O

G


Qualitative Architectural ImplicationsConsider both application characteristics and size requirement

• Long time scales (seconds to minutes)

• Accumulating values• Waiting for an event• Processing groups of

sensor values as an aggregate

• Trade area for time• Very simple core• Fixed point arithmetic• Higher precision

intermediary results• Single vs. group sensor

access

Applications Characteristics Architectural Implications


Nanoscale Sensor Processor Overview

• Integrated sensing and computing

• Single-accumulator– Reduce processor core complexity– Enable short 4-bit opcodes

• Memory mapped sensors– Unified instruction/data/sensor address space

Standard nSP

Address space 256 x 4 bit

Accumulator 16 bit

Operands 8 bit

Instruction length 4 bit and 12 bit


• Integrated sensing and computing

• Single-accumulator– Reduce processor core complexity– Enable short 4-bit opcodes

• Memory mapped sensors– Unified instruction/data/sensor address space

Standard nSP Tiny nSP

Address space 256 x 4 bit 16 x 4 bit

Accumulator 16 bit 8 bit

Operands 8 bit 4 bit

Instruction length 4 bit and 12 bit 4 bit and 8 bit

For smallest nSPs

Nanoscale Sensor Processor Overview


Integrated Sensing

• Exploit biological compatibility of entire system– RET: foundation for both sensing and computing

• Augment memory locations with sensing mechanism– Force value to “1” (or “0”)

• Instruction Fused Sensing (IFS)– Opcode sensitive to environment: e.g. JMP -> NOP on binding – Immediate sensitive to environment: e.g. ALU value, branch

target

• IFS can dramatically improve code density– Opportunity for hardware/software co-design

1 01 1 000 0 000 0 000 0

JMP 0

1

NOP

JMP 1 1 1 0

NOP 1 1 1 1

+ Sensed molecule

0 LD 993 NOT4 BNZ 0...99 sensorA

0 JMP (!A) 0

IFSLD/STAddr Addr

vs.


Example of Instruction Fused Sensing

while (true) dosample = read_sensor(P)if (sample != last_sample) do

count += samplelast_sample = sample

endif (send_data == true) do

output(count)count=0

endend

Word 0 JMP (!send) 6 3 OUTCLR 13 6 JMP (!A) 18 9 BNZ 0 12 INCI 15 JMP 0 18 CLR 19 JMP 0

8-bit counter pseudo-code

• Pathogen Counting

IFS

Word 0 LD sensorSend 3 BNZ 34 6 LD sensorA 9 BNZ 19 12 CLR 13 ST 42 16 JMP 0 19 LD 42 22 BNZ 0 25 INCI 28 ST 42 31 JMP 0 34 OUTCLR 26 37 JMP 6 40 sensorA 41 sensorSend 42 last

LD/ST

21.5 bytes vs. 11 bytes!


Node Size

• Preliminary layout for RET logic on DNA grid

• Standard nSP, 128 bytes of RAM: 2.5μm x 2.5μm– Can diffuse in biological micro-environments

• Tiny nSP, 8 bytes of RAM: 800nm x 800nm– Comparable in size with largest virus– Supports 4 out of the 5 applications – Reduced numerical range, number of sensors

Tiny nSP, 8bytes RAM

Standard nSP, 128bytes RAM

Intel 4004 (est. CMOS 32nm), no RAM *

~0.6 μm2 ~6 μm2 ~120 μm2

* without battery, sensors, converters, I/O transceivers, memory


Impact of IFS

• Non-IFS baseline implementation– sensors consume additional memory

– explicit LD/ST

• IFS reduces memory footprint between 58% and 5%

IFS vs. LD/ST

0

10

20

30

40

50

60

70

80

90

100

Counter Multi-A Kinetic FIR mw Avg Image

Application

Rel

ativ

e m

emo

ry f

oo

tpri

nt

(%)

IFS

LD/ST


Application Simulation Results

• Cycle-level nSP functional simulator– Single cycle memory access (4bit

word, including sensors)– Cycles per instruction: 2-6

• Chemical environment simulation– Time-varying concentrations

• Pathogen counting application– Single nSP, clockrate: 100Hz– nSP output correlated with

pathogen concentration

0

5

10

15

20

25

30

35

40

45

0 1000 2000 3000 4000 5000 6000

Time (s)

nS

P O

utp

ut (

Viru

s C

ou

nt)

0

200

400

600

800

1000

1200

0 1000 2000 3000 4000 5000 6000

Time (s)

Viru

s C

on

cen

tra

tion

([V

irus]

/uL

)


Required nSP Clock Frequency

• Constant pathogen concentration

• Minimum clock rate decided by sensor sampling rate

– Run fast enough to detect all binding events

0

5

10

15

20

25

30

35

40

45

0.1Hz 1Hz 10Hz 100Hz 1000Hz

nSP Clock Frequency

Vir

use

s D

etec

ted

(C

ou

nte

d)

Target


Wrap-up


– Scaffolds




• Conclusions


Conclusions

Demonstrated self-assembly of devices

30 nm

D

A A

D

R2

X

Y X Y R2/(R1+R2)R1


Conclusions

Material Science AdvancesImportant Problems in

Life Sciences

New Computational DomainBiological Scale Integrated Sensing and Computing

New challenges

Extreme size constraints

Nanoscale Sensor ProcessorsArchitecture and technology


DNA-based Spatial Computing:Toward Diffusion-limited Computation

Chris DwyerAssistant ProfessorDepartment of Electrical and Computer Engineering, Department of Computer Science

Spatial Computing Workshop / SASO, September 2009

http://nano.ece.duke.educhris dwyer, duke university dna-based spatial computing: toward...

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dna strand

dna double helix bform

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nm persistence length

time s dna

logic selfassembled