Fast Online Synthesis of Generally Programmable

Digital Microfluidic Biochips

Dan Grissom and Philip BriskUniversity of California, Riverside

CODES+ISSS (ESWEEK)Tampere, Finland, October 10

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The Future Of Chemistry

Microfluidics

Miniaturization + Automation

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Applications

Biochemical assays and immunoassaysClinical pathology

Drug discovery and testingRapid assay prototypingTesting new drugs (via lung-on-a-chip)

Biochemical terror and hazard detectionDNA extraction & sequencing

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Digital Microfluidic Biochips (DMFB) 101

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Resource-constrained scheduling of operations into time-stepsTime-step ~ scheduling unit, usually 1s or 2s

Placement of operations during each time-step into modulesModule ~ 2D group of cells where operation takes place for 1+ time-steps

Routing of droplets between operations between time-steps

DMFB High Level Synthesis

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Goal: Online SynthesisWhy: Programmability, control-flow, live-feedback

Problem: Past, optimized methods are too complexSolution: Synthesis with good results in little time

High Level Motivation

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Virtual topology applied to physical DMFBAll cells are physically the sameVirtual topology restricts location of operations (modules)Regular module placement

Solution: Virtual Topology

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Modules can be different sizesInput (output) ports on top/bottom left (right) cellsDroplets can wait in I/O cells as long as necessary

Arriving droplets will not interfere with departing droplets

Modules & Virtual I/O Ports

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Droplet Synchronization

Mix operation

Split operation

Storage operation

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Simplifies the synthesis processScheduling: Gives clear number of resources (no guessing)Placement: No placement; instead choose a free moduleRouting: Topology and module syncing guarantees routability

Why a Virtual Topology?

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We choose list-scheduling (LS) with several constraintsConstructive algorithm -- one iterationMuch faster than iterative algorithms (e.g., genetic algorithms)Total number of available resources dictated by virtual topology

Scheduling

Resources available each time-step2 general modules1 heating module1 detect module

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We convert placement into a binding problemModules are pre-placed at regular intervalsModule can be viewed as a fixed resource

It’s either available or not

Placement

Traditional Free Placement Proposed Fixed Placement

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Operations are bound to modules of the same typeWill never have to reschedule since fixed placement gave scheduler precise resource availability

Placement (…continued)

Ex: Time-step 2

M1T:[8-11)

R:B

M5T:[5-8)

R:B

M6T:[5-8)

R:B

M1T:[2-5)

R:B

M2T:[2-5)

R:B

M3T:[2-5)

R:H

M4T:[2-5)

R:D

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Greedy left-edge algorithm used for bindingOperations sorted by start time into module-type binsOperations bound greedily to specific modules

Placement (…continued)

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Simplified Soukup Maze Router [Roy, 2010]

Independent routes computed for each droplet

Routing

Independent routes

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Simplified Soukup Maze Router [Roy, 2010]

All routes compacted; stalls added if necessary

Routing (…continued)

- Droplets may collide if all start at same time

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Routing (…continued)

Independent routes compactedStalls added mid-route if possible

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4S1 1 2 3/6 4/5 5/4 6/3 2 1 S2

37 7

28 8

1T2 T1

0 1 2 3 4 5 6 7 8 9 10 11

Deadlock!

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Routing (…continued)

Independent routes compactedStalls added at beginning otherwise

Guaranteed to work because of designated module I/Os

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4S1 1 2 3/6 4/5 5/4 6/3 2 1 S2

37 7

28 8

1T2 T1

0 1 2 3 4 5 6 7 8 9 10 11

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Routing (…continued)

Sub-problem 1 Compaction

Sub-problem 2 Compaction

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Experimental ResultsCompared 2 flows

Our online: List SchedulerFixed BindingSimplified Maze Router

Traditional offline:Genetic SchedulerSimulated Annealing PlacerSimplified Maze Router

Used high-end and low-end platforms2.8GHz Intel Core i7, 4GB RAM, 64-bit Windows 71GHz Intel Atom, 512MB RAM, TimeSys 11 Linux

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BenchmarksPCR

In-Vitro Diagnostics5 different combos of samples/reagents

Colorimetric Protein

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Results - Scheduling

Genetic scheduling produces comparable schedules, but takes more time

PCR (OFF)

InVitro

_1 (O

N)

InVitro

_2 (O

FF)

InVitro

_3 (O

N)

InVitro

_4 (O

FF)

InVitro

_5 (O

N)

Protein

(OFF)

0

40,000

80,000

120,000

Genetic Scheduling vs. List Scheduling (Atom)

GA Sched GA Atom CPU LS Sched LS Atom CPU

Benchmark

Tim

e (m

s)

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Results - Placement

Our binder uses more space, but produces valid solutions in significantly less time

PCR (OFF)

InVitro

_1 (O

N)

InVitro

_2 (O

FF)

InVitro

_3 (O

N)

InVitro

_4 (O

FF)

InVitro

_5 (O

N)

Protein

(OFF)

020,000,00040,000,00060,000,00080,000,000

Simulated Annealing vs. Fixed Placement (Atom)

FP Atom CPU SAP Atom CPU

Benchmark

Tim

e (m

s)

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Results - Routing

Routing method is comparable for both flows

PCR (OFF)

InVitro

_1 (O

N)

InVitro

_2 (O

FF)

InVitro

_3 (O

N)

InVitro

_4 (O

FF)

InVitro

_5 (O

N)

Protein

(OFF)

0

4,000

8,000

12,000

Genetic Scheduling vs. List Scheduling (Atom)

OFF Route Length OFF Atom CPU ON Route Length ON Atom CPU

Benchmark

Tim

e (m

s)

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Results - Entire FlowAssay times (solutions) comparable for online/offline

Offline spends most of time computing synthesis

Online spends almost entire time running assay

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Conclusion

Presented a flow for online synthesisScheduling, placement and routing simplified by virtual topology

Scheduling: Know precise resource availabilityPlacement: Free placement simplified to bindingRouting: VT guarantees a deadlock-free route

Other fast scheduler/routers can be usedBiggest savings from fixed placement

Produces quality synthesis solutions in ms

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Microfluidics SimulatorOpen source release

www.microfluidics.cs.ucr.edu

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Thank You

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Synthesis Example: Scheduling

Scheduler

Input Assay (DAG)

Scheduled Assay (DAG)

DMFB Architecture

Placer

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Synthesis Example: Placement

Scheduled Assay (DAG)

Placement Information

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Synthesis Example: Routing

Placement Placement

Router

Final Output

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Maximum number of droplets permitted on DMFBLeave a “bubble” for a droplet Any droplet can be isolated in any module

Scheduling (…continued)

Results in scheduling DEADLOCK Results in schedulable configuration