Routing-Based Synthesis of Digital Microfluidic Biochips Elena Maftei, Paul Pop, Jan Madsen Technical University of Denmark CASES’101Routing-Based Synthesis.

Slides:



Advertisements
Similar presentations
Force-Directed List Scheduling for DMFBs Kenneth ONeal, Dan Grissom, Philip Brisk Department of Computer Science and Engineering Bourns College of Engineering.
Advertisements

NCKU CSIE EDALAB Shang-Tsung Yu, Sheng-Han Yeh, and Tsung-Yi Ho Electronic Design Automation Laboratory.
Autonomic Systems Justin Moles, Winter 2006 Enabling autonomic behavior in systems software with hot swapping Paper by: J. Appavoo, et al. Presentation.
Optimal Testing of Digital Microfluidic Biochips: A Multiple Traveling Salesman Problem R. Garfinkel 1, I.I. Măndoiu 2, B. Paşaniuc 2 and A. Zelikovsky.
Droplet-Aware Module-Based Synthesis for Fault-Tolerant Digital Microfluidic Biochips Elena Maftei, Paul Pop, and Jan Madsen Technical University of Denmark.
Improving Market-Based Task Allocation with Optimal Seed Schedules IAS-11, Ottawa. September 1, 2010 G. Ayorkor Korsah 1 Balajee Kannan 1, Imran Fanaswala.
1 of 30 June 14, 2000 Scheduling and Communication Synthesis for Distributed Real-Time Systems Paul Pop Department of Computer and Information Science.
Process Scheduling for Performance Estimation and Synthesis of Hardware/Software Systems Slide 1 Process Scheduling for Performance Estimation and Synthesis.
1 of 14 1/15 Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems Paul Pop, Petru Eles, Zebo Peng Embedded.
Scheduling with Optimized Communication for Time-Triggered Embedded Systems Slide 1 Scheduling with Optimized Communication for Time-Triggered Embedded.
1 of 16 March 30, 2000 Bus Access Optimization for Distributed Embedded Systems Based on Schedulability Analysis Paul Pop, Petru Eles, Zebo Peng Department.
1 of 12 May 3, 2000 Performance Estimation for Embedded Systems with Data and Control Dependencies Paul Pop, Petru Eles, Zebo Peng Department of Computer.
1 of 16 June 21, 2000 Schedulability Analysis for Systems with Data and Control Dependencies Paul Pop, Petru Eles, Zebo Peng Department of Computer and.
HW/SW Co-Synthesis of Dynamically Reconfigurable Embedded Systems HW/SW Partitioning and Scheduling Algorithms.
Applying Edge Partitioning to SPFD's 1 Applying Edge Partitioning to SPFD’s 219B Project Presentation Trevor Meyerowitz Mentor: Subarna Sinha Professor:
A Scheduling and Routing Algorithm for DMFS Ring Layouts with Bus-phase Addressing Megha Gupta Srinivas Akella.
Decoupling Capacitance Allocation for Power Supply Noise Suppression Shiyou Zhao, Kaushik Roy, Cheng-Kok Koh School of Electrical & Computer Engineering.
Torino (Italy) – June 25th, 2013 Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable Systems Christian Pilato Fabrizio Ferrandi,
Performance and Power Efficient On-Chip Communication Using Adaptive Virtual Point-to-Point Connections M. Modarressi, H. Sarbazi-Azad, and A. Tavakkol.
Tabu Search-Based Synthesis of Dynamically Reconfigurable Digital Microfluidic Biochips Elena Maftei, Paul Pop, Jan Madsen Technical University of Denmark.
Tradeoff Analysis for Dependable Real-Time Embedded Systems during the Early Design Phases Junhe Gan.
Petros OikonomakosBashir M. Al-Hashimi Mark Zwolinski Versatile High-Level Synthesis of Self-Checking Datapaths Using an On-line Testability Metric Electronics.
Recent Research and Emerging Challenges in the System-Level Design of Digital Microfluidic Biochips Paul Pop, Elena Maftei, Jan Madsen Technical University.
ROBUST RESOURCE ALLOCATION OF DAGS IN A HETEROGENEOUS MULTI-CORE SYSTEM Luis Diego Briceño, Jay Smith, H. J. Siegel, Anthony A. Maciejewski, Paul Maxwell,
Efficient Mapping onto Coarse-Grained Reconfigurable Architectures using Graph Drawing based Algorithm Jonghee Yoon, Aviral Shrivastava *, Minwook Ahn,
Mahesh Sukumar Subramanian Srinivasan. Introduction Embedded system products keep arriving in the market. There is a continuous growing demand for more.
Feedback Control for the Programmable Cell Culture Chip “ProCell” Felician Ștefan Blaga Supervisor: Paul Pop (DTU Informatics) Co-supervisors: Wajid Minhass.
Energy/Reliability Trade-offs in Fault-Tolerant Event-Triggered Distributed Embedded Systems Junhe Gan, Flavius Gruian, Paul Pop, Jan Madsen.
Path Scheduling on Digital Microfluidic Biochips Dan Grissom and Philip Brisk University of California, Riverside Design Automation Conference San Francisco,
Reconfigurable Computing Using Content Addressable Memory (CAM) for Improved Performance and Resource Usage Group Members: Anderson Raid Marie Beltrao.
Ping-Hung Yuh, Chia-Lin Yang, and Yao-Wen Chang
SVM-Based Routability-Driven Chip-Level Design for Voltage-Aware Pin-Constraint EWOD Chips Qin Wang 1, Weiran He, Hailong Yao 1, Tsung-Yi Ho 2, Yici Cai.
Tao Lin Chris Chu TPL-Aware Displacement- driven Detailed Placement Refinement with Coloring Constraints ISPD ‘15.
ILP-Based Pin-Count Aware Design Methodology for Microfluidic Biochips Chiung-Yu Lin and Yao-Wen Chang Department of EE, NTU DAC 2009.
Analysis and Optimization of Mixed-Criticality Applications on Partitioned Distributed Architectures Domițian Tămaș-Selicean, Sorin Ovidiu Marinescu and.
Exact routing for digital microfluidic biochips with temporary blockages OLIVER KESZOCZE ROBERT WILLE ROLF DRECHSLER ICCAD’14.
1. Placement of Digital Microfluidic Biochips Using the T-tree Formulation Ping-Hung Yuh 1, Chia-Lin Yang 1, and Yao-Wen Chang 2 1 Dept. of Computer Science.
A SAT-Based Routing Algorithm for Cross-Referencing Biochips Ping-Hung Yuh 1, Cliff Chiung-Yu Lin 2, Tsung- Wei Huang 3, Tsung-Yi Ho 3, Chia-Lin Yang 4,
Resource Mapping and Scheduling for Heterogeneous Network Processor Systems Liang Yang, Tushar Gohad, Pavel Ghosh, Devesh Sinha, Arunabha Sen and Andrea.
Mobile Agent Migration Problem Yingyue Xu. Energy efficiency requirement of sensor networks Mobile agent computing paradigm Data fusion, distributed processing.
1 SYNTHESIS of PIPELINED SYSTEMS for the CONTEMPORANEOUS EXECUTION of PERIODIC and APERIODIC TASKS with HARD REAL-TIME CONSTRAINTS Paolo Palazzari Luca.
Task Graph Scheduling for RTR Paper Review By Gregor Scott.
Johnathan Fiske, *Dan Grissom, Philip Brisk
MROrder: Flexible Job Ordering Optimization for Online MapReduce Workloads School of Computer Engineering Nanyang Technological University 30 th Aug 2013.
Fast Online Synthesis of Generally Programmable Digital Microfluidic Biochips Dan Grissom and Philip Brisk University of California, Riverside CODES+ISSS.
1 of 16 April 25, 2006 System-Level Modeling and Synthesis Techniques for Flow-Based Microfluidic Large-Scale Integration Biochips Contact: Wajid Hassan.
Practical Message-passing Framework for Large-scale Combinatorial Optimization Inho Cho, Soya Park, Sejun Park, Dongsu Han, and Jinwoo Shin KAIST 2015.
Wajid Minhass, Paul Pop, Jan Madsen Technical University of Denmark
Synthesis of Communication Schedules for TTEthernet-based Mixed-Criticality Systems Domițian Tămaș-Selicean 1, Paul Pop 1 and Wilfried Steiner 2 1 Technical.
1 Implicant Expansion Methods Used in The BOOM Minimizer Petr Fišer, Jan Hlavička Czech Technical University, Karlovo nám. 13, Prague 2
ILP-Based Synthesis for Sample Preparation Applications on Digital Microfluidic Biochips ABHIMANYU YADAV, TRUNG ANH DINH, DAIKI KITAGAWA AND SHIGERU YAMASHITA.
Output Grouping-Based Decomposition of Logic Functions Petr Fišer, Hana Kubátová Department of Computer Science and Engineering Czech Technical University.
1 Hardware-Software Co-Synthesis of Low Power Real-Time Distributed Embedded Systems with Dynamically Reconfigurable FPGAs Li Shang and Niraj K.Jha Proceedings.
System-Level Modeling and Simulation of the Cell Culture Microfluidic Biochip ProCell Wajid Hassan Minhass †, Paul Pop †, Jan Madsen † Mette Hemmingsen.
Synthesis of Digital Microfluidic Biochips with Reconfigurable Operation Execution Elena Maftei Technical University of Denmark DTU Informatics
Task Mapping and Partition Allocation for Mixed-Criticality Real-Time Systems Domițian Tămaș-Selicean and Paul Pop Technical University of Denmark.
Synthesis of Reliable Digital Microfluidic Biochips using Monte Carlo Simulation Elena Maftei, Paul Pop, Florin Popenţiu Vlădicescu Technical University.
Optimization of Time-Partitions for Mixed-Criticality Real-Time Distributed Embedded Systems Domițian Tămaș-Selicean and Paul Pop Technical University.
Synthesis of Biochemical Applications on Digital Microfluidic Biochips with Operation Variability Mirela Alistar, Elena Maftei, Paul Pop, Jan Madsen.
1 Placement-Aware Architectural Synthesis of Digital Microfluidic Biochips using ILP Elena Maftei Institute of Informatics and Mathematical Modelling Technical.
CHaRy Software Synthesis for Hard Real-Time Systems
Contents Introduction Bus Power Model Related Works Motivation
Prabhat Kumar Saraswat Paul Pop Jan Madsen
Paul Pop, Petru Eles, Zebo Peng
Architecture Synthesis for Cost Constrained Fault Tolerant Biochips
Elena Maftei Technical University of Denmark DTU Informatics
Fault-Tolerant Architecture Design for Flow-Based Biochips
Timing Analysis of Mixed-Criticality Hard Real-Time Applications Implemented on Distributed Partitioned Architectures Sorin Ovidiu Marinescu, Domițian.
Microfluidic Biochips
The use of Neural Networks to schedule flow-shop with dynamic job arrival ‘A Multi-Neural Network Learning for lot Sizing and Sequencing on a Flow-Shop’
Presentation transcript:

Routing-Based Synthesis of Digital Microfluidic Biochips Elena Maftei, Paul Pop, Jan Madsen Technical University of Denmark CASES’101Routing-Based Synthesis of Digital Microfluidic Biochips

Microfluidic Biochips CASES’102Routing-Based Synthesis of Digital Microfluidic Biochips Technical Univ. of Denmark 2010 Duke University 2002 Continuous-flow biochips Droplet-based biochips

Microfluidic Biochips  Applications  Sampling and real time testing of air/water for biochemical toxins  Detection of adverse atmospheric conditions  DNA analysis and sequencing  Clinical diagnosis  Point of care devices  Advantages:  High throughput (reduced sample / reagent consumption)‏  Space (miniaturization)‏  Time (parallelism)‏  Automation (minimal human intervention)‏ CASES’103Routing-Based Synthesis of Digital Microfluidic Biochips

Outline  Motivation  Architecture  Typical Design Tasks  Problem Formulation  Proposed Solution  GRASP-Based Synthesis  Experimental Evaluation  Conclusions CASES’104Routing-Based Synthesis of Digital Microfluidic Biochips

Biochip Architecture Biochip created at Duke University CASES’105Routing-Based Synthesis of Digital Microfluidic Biochips

Electrowetting on Dielectric CASES’106Routing-Based Synthesis of Digital Microfluidic Biochips

Module-Based Synthesis: Design Tasks Scheduling Binding Placement Allocation CASES’107Routing-Based Synthesis of Digital Microfluidic Biochips

Module-Based Synthesis CASES’108Routing-Based Synthesis of Digital Microfluidic Biochips

Module-Based Synthesis O7 O8 O9 CASES’109Routing-Based Synthesis of Digital Microfluidic Biochips

Module-Based Synthesis O7 O8 O9 CASES’1010Routing-Based Synthesis of Digital Microfluidic Biochips

Module-Based Synthesis O71x4 O81x4 CASES’1011Routing-Based Synthesis of Digital Microfluidic Biochips

Module-Based Synthesis O92x4 O101x4 CASES’1012Routing-Based Synthesis of Digital Microfluidic Biochips

Module-Based Synthesis O92x4 O101x4 CASES’1013Routing-Based Synthesis of Digital Microfluidic Biochips

Module-Based Synthesis CASES’1014Routing-Based Synthesis of Digital Microfluidic Biochips

Mixing  Droplets can move anywhere  Fixed area: module-based synthesis  Unconstrained: routing-based synthesis CASES’1015Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1016Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis O7 O8 O9 CASES’1017Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1018Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1019Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1020Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1021Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1022Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1023Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1024Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1025Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1026Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1027Routing-Based Synthesis of Digital Microfluidic Biochips

When will the operations complete?  For module-based synthesis we know the completion time from the module library.  But now there are no modules, the droplets can move anywhere.  How can we find out the operation completion times? CASES’10Routing-Based Synthesis of Digital Microfluidic Biochips28

Characterizing operations  If the droplet does not move: very slow mixing by diffusion  If the droplet moves, how long does it take to complete?  Mixing percentages: p 0, p 90, p 180 ? CASES’1029Routing-Based Synthesis of Digital Microfluidic Biochips

Characterizing operations  We know how long an operation takes on modules  Starting from this, can determine the percentages? CASES’1030Routing-Based Synthesis of Digital Microfluidic Biochips

Decomposing modules Safe, conservative estimates p 90 =0.1%, p 180 =-0.5%, p 0 =0.29% and 0.58% CASES’1031Routing-Based Synthesis of Digital Microfluidic Biochips Moving a droplet one cell takes 0.01 s.

Routing-Based Synthesis CASES’1032Routing-Based Synthesis of Digital Microfluidic Biochips

Routing-Based Synthesis CASES’1033Routing-Based Synthesis of Digital Microfluidic Biochips

Droplet- vs. Module-Based Synthesis Module-Based SynthesisRouting-Based Synthesis CASES’1034Routing-Based Synthesis of Digital Microfluidic Biochips

Problem Formulation  Input  Application graph  Architecture (matrix of electrodes)  Library of non-reconfigurable devices  Output  Implementation which minimizes application execution time  Droplet routes for all reconfigurable operations  Allocation and binding of non-reconfigurable modules from a library  Scheduling of operations CASES’1035Routing-Based Synthesis of Digital Microfluidic Biochips

Proposed Solution CASES’1036Routing-Based Synthesis of Digital Microfluidic Biochips

Proposed Solution Meet Execute CASES’1037Routing-Based Synthesis of Digital Microfluidic Biochips

Proposed Solution Meet Execute Minimize the time until the droplet(s) arrive at destination Minimize the completion time for the operation CASES’1038Routing-Based Synthesis of Digital Microfluidic Biochips

GRASP-Based Heuristic  Greedy Randomized Adaptive Search Procedure CASES’1039Routing-Based Synthesis of Digital Microfluidic Biochips

GRASP-Based Heuristic  Greedy Randomized Adaptive Search Procedure CASES’1040Routing-Based Synthesis of Digital Microfluidic Biochips

GRASP-Based Heuristic  Greedy Randomized Adaptive Search Procedure For each droplet: Determine possible moves Evaluate possible moves Make a list of best N possible moves Perform a randomly chosen possible move CASES’1041Routing-Based Synthesis of Digital Microfluidic Biochips

GRASP-Based Heuristic Greedy Randomized Adaptive Search Procedure For each droplet: Determine possible moves Evaluate possible moves Make a list of best N possible moves Perform a randomly chosen possible move CASES’1042Routing-Based Synthesis of Digital Microfluidic Biochips

GRASP-Based Heuristic Greedy Randomized Adaptive Search Procedure For each droplet: Determine possible moves Evaluate possible moves Make a list of best N possible moves Perform a randomly chosen possible move CASES’1043Routing-Based Synthesis of Digital Microfluidic Biochips

GRASP-Based Heuristic Greedy Randomized Adaptive Search Procedure For each droplet: Determine possible moves Evaluate possible moves Make a list of best N possible moves Perform a randomly chosen possible move CASES’1044Routing-Based Synthesis of Digital Microfluidic Biochips

GRASP-Based Heuristic Greedy Randomized Adaptive Search Procedure For each droplet: Determine possible moves Evaluate possible moves Make a list of best N possible moves Perform a randomly chosen possible move CASES’1045Routing-Based Synthesis of Digital Microfluidic Biochips

Experimental Evaluation Routing-Based Synthesis (RBS) vs. to Module-Based Synthesis* (MBS) Two real-life applications (the table below) Ten synthetic benchmarks (see the paper) (*) E. Maftei at al.: Tabu Search-Based Synthesis of Dynamically Reconfigurable Digital Microfluidic Biochips. CASES, 2009 (best paper award) CASES’1046Routing-Based Synthesis of Digital Microfluidic Biochips

Contributions and Message  Message  Labs-on-a-chip can be implemented with microfluidics  CAD tools are essential for the design of biochips  Contributions  Eliminated the concept of “virtual modules” and proposed a routing-based synthesis approach  Proposed a GRASP-based algorithm for showing that routing- based synthesis leads to significant improvements compared to module-based synthesis CASES’1047Routing-Based Synthesis of Digital Microfluidic Biochips

Discussion  Module-based vs. routing-based  Module-based needs an extra routing step between the modules; Routing-based performs unified synthesis and routing  Module-based wastes space: only one module-cell is used; Routing-based exploits better the application parallelism  Module-base can contain the contamination to a fixed area;  We are extending routing-based to consider contamination  Module-based: nice, useful abstraction?  Routing based: too much spaghetti? CASES’10Routing-Based Synthesis of Digital Microfluidic Biochips48