Path Scheduling on Digital Microfluidic Biochips Dan Grissom and Philip Brisk University of California, Riverside Design Automation Conference San Francisco,

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Presentation transcript:

Path Scheduling on Digital Microfluidic Biochips Dan Grissom and Philip Brisk University of California, Riverside Design Automation Conference San Francisco, CA, USA, June 5

2 Digital Microfluidic Biochips (DMFB) 101

Resource-constrained scheduling of operations into time-steps Time-step ~ scheduling unit, usually 1s or 2s Placement of operations during each time-step into modules Module ~ 2D group of cells where operation takes place for 1+ time-steps Routing of droplets between operations between time-steps 3 DMFB Synthesis

Goal: Online Synthesis Why: Programmability, control-flow, live-feedback Problem: Past, optimized methods are too complex Solution: Synthesis with good results in little time 4 High Level Motivation [Luo et al., DATE 2012]

DMFBs have limited storage Unlike computers, same resources used for operations and storage Storage takes up space that can be used by useful operations Scheduling Goal: Minimize storage Introduce droplets to system ALAP Use stored droplets ASAP to free space on array 5 DMFB Scheduling Problem VS. Extra Resource

A path is a series of nodes that terminates with a mix/output Paths are not unique 6 Colorimetric Protein AssayPCR Mixing Stage Assay Schedule By Path

Defined as the cumulative number of droplets being output in a node’s fan-out IPP helps determine which node/path to schedule next 7 Colorimetric Protein AssayPCR Mixing Stage Assay Independent Path Priority (IPP)

Independent Path Priorities Live Example Legend Path leader (not on current path) Potential next-node-on-path Fully scheduled/submitted path Scheduling Example

9 Solution QualityComputational Complexity Storage Usage Lower is better for all metrics # Assays Concurrent Scheduled Protein Results Implemented 3 schedulers in C++ Path Scheduler List-scheduler Decreasing critical-path priorities (worse) Increasing critical-path priorities (better)

10 MLS_DEC 252 storage nodes MLS_INC 119 storage nodes PS 54 storage nodes Black node = droplet being stored for a # of time-steps in 1 module Placed Protein DAG Showing Storage

More results in the paper/poster Excels at assays with high fan-out and limited resources Path scheduler saves 100’s of seconds Performs well on assays w/o high fan-out Similar schedule quality Slightly shorter computation time Viable option for online synthesis 11 Conclusion

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