Advanced Computer Architecture & Processing Systems Research Lab Framework for Automatic Design Space Exploration.

Slides:



Advertisements
Similar presentations
Improving Learning Object Description Mechanisms to Support an Integrated Framework for Ubiquitous Learning Scenarios María Felisa Verdejo Carlos Celorrio.
Advertisements

D ARMSTADT, G ERMANY - 11/07/2013 A Framework for Effective Exploitation of Partial Reconfiguration in Dataflow Computing Riccardo Cattaneo ∗, Xinyu Niu†,
MOEAs University of Missouri - Rolla Dr. T’s Course in Evolutionary Computation Matt D. Johnson November 6, 2006.
Angers, 10 June 2010 Multi-Objective Optimisation (II) Matthieu Basseur.
Constructing Complex NPC Behavior via Multi- Objective Neuroevolution Jacob Schrum – Risto Miikkulainen –
Architecture for Exploring Large Design Spaces John R. Josephson, B. Chandrasekaran, Mark Carroll, Naresh Iyer, Bryon Wasacz, Qingyuan Li, Giorgio Rizzoni,
Master/Slave Architecture Pattern Source: Pattern-Oriented Software Architecture, Vol. 1, Buschmann, et al.
Part2 AI as Representation and Search
T-FLEX DOCs PLM, Document and Workflow Management.
Integrating Bayesian Networks and Simpson’s Paradox in Data Mining Alex Freitas University of Kent Ken McGarry University of Sunderland.
Applying Genetic Algorithms to Decision Making in Autonomic Computing Systems Authors: Andres J. Ramirez, David B. Knoester, Betty H.C. Cheng, Philip K.
1 HW/SW Partitioning Embedded Systems Design. 2 Hardware/Software Codesign “Exploration of the system design space formed by combinations of hardware.
An Integrated Framework for Dependable Revivable Architectures Using Multi-core Processors Weiding Shi, Hsien-Hsin S. Lee, Laura Falk, and Mrinmoy Ghosh.
Rick Kuhn Computer Security Division
Automating Keyphrase Extraction with Multi-Objective Genetic Algorithms (MOGA) Jia-Long Wu Alice M. Agogino Berkeley Expert System Laboratory U.C. Berkeley.
A General approach to MPLS Path Protection using Segments Ashish Gupta Ashish Gupta.
Workshop on Cyber Infrastructure in Combustion Science April 19-20, 2006 Subrata Bhattacharjee and Christopher Paolini Mechanical.
IE 594 : Research Methodology – Discrete Event Simulation David S. Kim Spring 2009.
Genetic Algorithms: A Tutorial
1 Reasons for parallelization Can we make GA faster? One of the most promising choices is to use parallel implementations. The reasons for parallelization.
IMPLEMENTATION ISSUES REGARDING A 3D ROBOT – BASED LASER SCANNING SYSTEM Theodor Borangiu, Anamaria Dogar, Alexandru Dumitrache University Politehnica.
Using Provenance to Support Real-Time Collaborative Design of Workflows Tommy Ellkvist 1, Erik Anderson 2, David Koop 2, Juliana Freire 2, and Claudio.
Evolving Multi-modal Behavior in NPCs Jacob Schrum – Risto Miikkulainen –
A Scalable Application Architecture for composing News Portals on the Internet Serpil TOK, Zeki BAYRAM. Eastern MediterraneanUniversity Famagusta Famagusta.
Constraints-based Motion Planning for an Automatic, Flexible Laser Scanning Robotized Platform Th. Borangiu, A. Dogar, A. Dumitrache University Politehnica.
Supervised Design Space Exploration by Compositional Approximation of Pareto Sets Hung-Yi Liu 1, Ilias Diakonikolas 2, Michele Petracca 1, and Luca P.
Design Space Exploration
Some Thoughts on HPC in Natural Language Engineering Steven Bird University of Melbourne & University of Pennsylvania.
Distributed Protein Structure Analysis By Jeremy S. Brown Travis E. Brown.
Energy saving in multicore architectures Assoc. Prof. Adrian FLOREA, PhD Prof. Lucian VINTAN, PhD – Research.
Mahesh Sukumar Subramanian Srinivasan. Introduction Embedded system products keep arriving in the market. There is a continuous growing demand for more.
Inferring Temporal Properties of Finite-State Machines with Genetic Programming GECCO’15 Student Workshop July 11, 2015 Daniil Chivilikhin PhD student.
Integrated Maximum Flow Algorithm for Optimal Response Time Retrieval of Replicated Data Nihat Altiparmak, Ali Saman Tosun The University of Texas at San.
Robin McDougall Scott Nokleby Mechatronic and Robotic Systems Laboratory 1.
F. Gharsalli, S. Meftali, F. Rousseau, A.A. Jerraya TIMA laboratory 46 avenue Felix Viallet Grenoble Cedex - France Embedded Memory Wrapper Generation.
Frankfurt (Germany), 6-9 June 2011 Steven Inglis – United Kingdom – RIF Session 5 – Paper 0434 Multi-Objective Network Planning tool for the optimal integration.
1 “Genetic Algorithms are good at taking large, potentially huge search spaces and navigating them, looking for optimal combinations of things, solutions.
Simulation in Wind Turbine Vibrations: A Data Driven Analysis Graduate Students: Zijun Zhang PI: Andrew Kusiak Intelligent Systems Laboratory The University.
EPA Enterprise Data Architecture Metadata Framework Assessment Kevin J. Kirby, Enterprise Data Architect EPA Enterprise Architecture Team
Institute for Software Integrated Systems Vanderbilt University DARPA ASC PI Meeting May 26-28, 1999 Adaptive Model-Integrated Computing Akos Ledeczi.
Grid programming with components: an advanced COMPonent platform for an effective invisible grid © 2006 GridCOMP Grids Programming with components. An.
FORS 8450 Advanced Forest Planning Lecture 5 Relatively Straightforward Stochastic Approach.
Kanpur Genetic Algorithms Laboratory IIT Kanpur 25, July 2006 (11:00 AM) Multi-Objective Dynamic Optimization using Evolutionary Algorithms by Udaya Bhaskara.
A Systematic Approach to the Design of Distributed Wearable Systems Urs Anliker, Jan Beutel, Matthias Dyer, Rolf Enzler, Paul Lukowicz Computer Engineering.
Intelligent Database Systems Lab Presenter : Kung, Chien-Hao Authors : Eghbal G. Mansoori 2011,IEEE FRBC: A Fuzzy Rule-Based Clustering Algorithm.
2/29/20121 Optimizing LCLS2 taper profile with genetic algorithms: preliminary results X. Huang, J. Wu, T. Raubenhaimer, Y. Jiao, S. Spampinati, A. Mandlekar,
High Performance Embedded Computing © 2007 Elsevier Chapter 7, part 3: Hardware/Software Co-Design High Performance Embedded Computing Wayne Wolf.
Implementation of a Relational Database as an Aid to Automatic Target Recognition Christopher C. Frost Computer Science Mentor: Steven Vanstone.
Evolving Multimodal Networks for Multitask Games
1 Advanced Software Architecture Muhammad Bilal Bashir PhD Scholar (Computer Science) Mohammad Ali Jinnah University.
Multi-objective Topology Synthesis and FPGA Prototyping Framework of Application Specific Network-on-Chip m Akram Ben Ahmed Xinyu LI, Omar Hammami.
Mapping of Regular Nested Loop Programs to Coarse-grained Reconfigurable Arrays – Constraints and Methodology Presented by: Luis Ortiz Department of Computer.
Evolutionary multi-objective algorithm design issues Karthik Sindhya, PhD Postdoctoral Researcher Industrial Optimization Group Department of Mathematical.
Multi-Objective Optimization for Topology Control in Hybrid FSO/RF Networks Jaime Llorca December 8, 2004.
1 ParadisEO-MOEO for a Bi-objective Flow-Shop Scheduling Problem May 2007 E.-G. Talbi and the ParadisEO team
Parallelizing Functional Tests for Computer Systems Using Distributed Graph Exploration Alexey Demakov, Alexander Kamkin, and Alexander Sortov
Use of Performance Prediction Techniques for Grid Management Junwei Cao University of Warwick April 2002.
Introduction to Performance Tuning Chia-heng Tu PAS Lab Summer Workshop 2009 June 30,
System on a Programmable Chip (System on a Reprogrammable Chip)
PLM, Document and Workflow Management
IP – Based Design Methodology
Gabor Madl Ph.D. Candidate, UC Irvine Advisor: Nikil Dutt
Design Space Exploration
Multi-Objective Optimization for Topology Control in Hybrid FSO/RF Networks Jaime Llorca December 8, 2004.
Multi-Objective Optimization
Presented By: Darlene Banta
Stefan Oßwald, Philipp Karkowski, Maren Bennewitz
Cloud-DNN: An Open Framework for Mapping DNN Models to Cloud FPGAs
T-FLEX DOCs PLM, Document and Workflow Management.
Search-Based Approaches to Accelerate Deep Learning
Presentation transcript:

Advanced Computer Architecture & Processing Systems Research Lab Framework for Automatic Design Space Exploration of Computer Systems Horia Calborean Prof. Lucian Vinţan

2 Advanced Computer Architecture & Processing Systems Research Lab Outline Design space exploration  Multi-objective optimization  Metrics used Methodology and tools  Framework for Automatic Design Space Exploration (FADSE)  GAP Results  Results reuse  Algorithm comparison Conclusions

3 Advanced Computer Architecture & Processing Systems Research Lab Design space exploration (DSE) Number of architectural parameters has risen Huge number of possible configurations  50 parameters with 8 values => possible configurations Exhaustive evaluation impossible Manual design space exploration infeasible Solution: heuristic search algorithms

4 Advanced Computer Architecture & Processing Systems Research Lab Multi-objective DSE Performance evaluation has become a complex multi-objective evaluation (speed, power consumption, area integration, etc.) Multi-objective search algorithms are used Problem: no order can be established between the individuals

5 Advanced Computer Architecture & Processing Systems Research Lab Basic notions about Pareto front

6 Advanced Computer Architecture & Processing Systems Research Lab Metrics used: hypervolume Does not require the true Pareto front to be known Volume enclosed by:  the current Pareto front approximation and  the hypervolume reference point

7 Advanced Computer Architecture & Processing Systems Research Lab Metrics used: coverage of two sets Returns the fraction of individuals produced by one algorithm that dominates individuals produced by the other algorithm.

8 Advanced Computer Architecture & Processing Systems Research Lab FADSE Integrates many DSE algorithms (through jMetal library):  NSGA-II, SPEA2, SMPSO, OMOPSO, etc. Can connect to many simulators: M5, MSIM2, MSIM3, Multi2Sim, GAP, GAPtimize, UniMap Other simulators can be easily integrated

9 Advanced Computer Architecture & Processing Systems Research Lab Accelerating the DSE process: parallel evaluation Client-server application Evaluations are one in parallel Results are sent back asynchronous At the end of the generation: synchronization point

10 Advanced Computer Architecture & Processing Systems Research Lab Accelerating the DSE process: results reuse Algorithms tend to generate the same individuals again  Can be reused => avoid simulation Can use results from previous explorations

11 Advanced Computer Architecture & Processing Systems Research Lab Reliable Clients / simulators crash:  Implemented watchdog timer Network connection lost:  Server resends simulations Server crashed, power loss:  Implemented checkpoint mechanism

12 Advanced Computer Architecture & Processing Systems Research Lab Easily configurable XML interface:  Describe the parameters for the connector  Describe the parameters for the simulator Arithmetic progression, geometric progression (ratio 2), list of strings  Configure database connection  Specify constraints

13 Advanced Computer Architecture & Processing Systems Research Lab Constraints specification  

14 Advanced Computer Architecture & Processing Systems Research Lab FADSE

15 Advanced Computer Architecture & Processing Systems Research Lab Grid Alu Processor (GAP)  Novel processor architecture from the University of Augsburg, combines coarse-grained reconfigurable array of functional units with superscalar-like frontend  Design space of over 1.1*10 6 Two objectives to be minimized: speed (CPI) and complexity (Jahr et al.(2011))

16 Advanced Computer Architecture & Processing Systems Research Lab This work  Analyze the influence of the results reuse on the DSE process  Comparison of three well known DSE algorithms

17 Advanced Computer Architecture & Processing Systems Research Lab Number of simulated individuals VS number of generated individuals – NSGA-II 60% reuse after 100 generations

18 Advanced Computer Architecture & Processing Systems Research Lab New individuals generated VS new individuals added to the next population

19 Advanced Computer Architecture & Processing Systems Research Lab Hypervolume on GAP

20 Advanced Computer Architecture & Processing Systems Research Lab Coverage NSGA-II and SPEA2 on GAP

21 Advanced Computer Architecture & Processing Systems Research Lab Coverage NSGA-II and SMPSO on GAP

22 Advanced Computer Architecture & Processing Systems Research Lab Pareto front approximation over the generations

23 Advanced Computer Architecture & Processing Systems Research Lab Conclusions SMPSO finds better results Database integration allows a faster DSE process FADSE is a flexible tool:  Many algorithms can be selected  Connects to many simulators

24 Advanced Computer Architecture & Processing Systems Research Lab Further work Insert known good configurations at the beginning of the search Domain knowledge using fuzzy rules, constraints Integration with UniMap

Advanced Computer Architecture & Processing Systems Research Lab Thank you Questions