Intelligent Systems (IS) Computer Systems Architecture (CSA)
Focus Areas
Introduction for Prospective Graduate Students
Ian Walker
Fall 2012
Outline
Who and what are we? Classes, requirements, planning Funding opportunities, assistantships Degree options Sample research projects Q&A
Who are we?
Loose confederation based upon common research interests
Loose mission statements: IS: Building smarter machine systems CSA: Building better/faster computing machines
Who: IS (9 Professors): Birchfield, Brooks, Burg, Dawson, Groff, Hoover,
Schalkoff, Venayagamoorthy (new!), Walker CSA (9 Professors): Birchfield, Brooks, Gowdy, Hoover, Ligon,
Schalkoff, Shen, Smith, Walker
Who are we?
Current enrollmentIS: 30-50 graduate studentsCSA: 10-25 graduate students
Lab spaceIS: Riggs 10, Riggs 13/15/17 (main lab), EIB 258 (main lab)CSA: Riggs 309 (main lab), EIB 352 (main lab), Cluster roomShared: Riggs 315/7, EIB 341, ...
Sample Research Areas
Sensor networks Tracking filters and embedded systems Physiological monitoring systems Nonlinear system modeling and control Audio and visual spatial sensing Biologically inspired robotics (More that are not listed here)
Classes (IS)
Required (all these courses offered once per year) :ECE 801 - Analysis of Linear SystemsECE 847 - Digital Image ProcessingA 600-level course chosen from (642, 655*, 668)One of (854, 855, 856, 868, 869, 872, 874*, 877)
*For Computer Engineering, 649 replaces 655, and 874 is removed from list
Other IS courses (typically offered once per 3 semesters):804, 805, 854, 856, 872, 893 (various)courses from other focus areas or departments are allowed
Planning: Take core early, figure out what you would like to do
See p. 35 of http://www.clemson.edu/ces/departments/ece/document_resource/grad/Grad_Student_Handbook_2011
Classes (CSA)
Required:A software course (ECE 617, 852, 855, or 873)An architecture course (ECE 629, 668, 842, or 851)A networks course (ECE 640, 649, 848, or 849)
Other CSA courses:any from the above listscourses from other focus areas or departments are allowed
Note: 693 and 893 are used for new courses. Be sure to sign up for the right section number.
See p. 32 of http://www.clemson.edu/ces/departments/ece/document_resource/grad/Grad_Student_Handbook_2011
Advisors
Selecting a faculty advisor is a two-way decision
All faculty use different criteria for evaluating studentsPerformance in core course taught by that professorEvaluation of volunteer or startup work in labProbationary periodAssistance to PhD or senior graduate student
Funding
Grading Assistantship (GA) - assist prof. with a courseTeaching Assistantship (TA) - teach lab sectionsResearch Assistantship (RA) - assist prof. in funded project
GAs and TAs are administered by department
RAs are generally offered to PhD students, or sometimes masters students showing potential and commitment for PhD
You do not need funding to get involved in research
Degree options
Majors (at masters and PhD level):Computer Engineering (CpE)
Electrical Engineering (EE)
Options:Focus area (IS is one of six areas in department)
Non-thesis (coursework only)
33 hours (11 courses)
Thesis
30 hours (8 courses + research)
best to examine options after first semester completed
typically work with PhD student
probably adds a semester - 2 years total
Direct-PhD
60 hours (14 courses + research)
saves 2 courses compared with Masters + PhD
possible to get an MS along the way
For details, see http://www.clemson.edu/ces/departments/ece/document_resource/grad/Grad_Student_Handbook_2011
Recent graduates
Ph.D. students - Academic PositionsClarkson University at Potsdam, New York
University of Michigan at Ann Arbor, Michigan
Louisiana State University, Louisiana
University of Florida
Ph.D. students - Industrial PositionsLucent Technology in Connecticut
Oakridge National Laboratories in Tennessee
Mayo Clinic in Minnesota
MS Students - Ph.D. PursuitsGerman Aerospace Institute in Germany
Stanford University in California
MS Students - Industrial PositionsGeneral Electric in Virginia
IBM in North Carolina
Intel in Columbia and San Francisco
Yahoo! in California
Harris in Florida
GM-Fanuc in Michigan
Name: Kumar Venayagamoorthy
Focus Area: Power/IS
http://www.people.clemson.edu/~gvenaya/
Research Area:
Real-time power systems
Current Projects:
Smart Grid research
Name: Richard Brooks
Focus Area: CSA/IS
http://www.clemson.edu/~rrb
Research Area:
Distributed Systems / Information Assurance / Coordination
Current Projects:
AFOSR – Detection of Tunnelled Communications Protocols
Industry – Data Leak Prevention
NSF – Network Security Experimentation with GENI
Department of State – Internet Liberty Support for West Africa
Relevant courses:
ECE449 / 649
Data Leak Prevention (DLP) solutions monitor and control data flow
Current DLP solutions are syntax based
We focus on data semanticsSingular value-based approach
Apply singular value decomposition to term-document matrix.
Find concepts by retaining a number of dimensions.
Hidden Markov Model (HMM)-based approachBuild HMMs based on terms we retained in singular value-based method.
Find transition probabilities of each document and estimate the probabilities of unobserved transitions.
Probabilistic Context-Free Grammar (PCFG)-based approachObtain parse trees of sentences in training documents.
Identify features in the parse trees.
- Hash functions - Regular expressions
- Keyword search
- Hash functions - Regular expressions
- Keyword search
Singular valuedecomposition
TransmissionCacheVLSI…….
• WiMAX BCR System Parameters and DDoS Attack Analysis- Factorial Experimental Design and ANOVA analysis of avg. throughput Ns-2 simulator used for software
simulations- Real software-defined radio testbeds used for hardware simulations
• Performance Analysis of DDoS Detection Methods on Operational Network- Setup the network using Clemson University GENI resources.- Use Operational Network traffic.- Generate DDoS attack traffic using Clemson Condor Cluster.- Analyze performance of DDoS detection methods.
Distributed Denial of Service Attack (DDoS) Analysis
• A bootable USB drive with the Linux system will access the proxy network.• The proxy network deploys botnet which changes DNS and IP address to avoid detection and tracking.• With this, the democracy advocates, NGOs, and journalists are protected from network censorship and
surveillance.
• Protocol analysis of Tor through side-channel attacks– Protocol represented as a hidden Markov model (HMM)– Side-channel information: delays between packets– Using zero-knowledge HMM inference algorithm to rebuild the model, i.e. the protocol used by A.
• Botnet traffic detection- Infer HMMs from botnet timing data- Use confidence interval approach to detect botnet traffic- Result: 95% TP and 2% FP
Detecting Hidden Communications Protocols
Name: Melissa Smith
Focus Area: CSA
http://www.parl.clemson.edu/~smithmc/
Research Area:
High-Performance Reconfigurable Computing/ Heterogeneous Computing
Current Projects:
Heterogeneous Mapping and Acceleration of Scientific Algorithms
Acceleration of Gene Co-Expression Network Generation
Performance Models for Hybrid Computing
Exploration of Concurrent Biometric Algorithms for Emerging Reconfigurable Architectures
Relevant courses: (ECE 668, 845, 842, 873, 893)
Spiking Neural Networks (SNN): preferred neural network models for simulating the biological behavior of a neuron
Ultimate goal of scientists:Model mammalian brain activity(1011 neurons – 1014 synapses)
Object recognition/identification
SNNs Optimizations with Multi-Core Architectures
Two-level character recognition network w/ two SNN models:
Izhikevich’s Model Flop/Byte : 0.65
Wilson model: Flop/Byte: 0.86
Morris Lecar Model Flop/Byte:4.71
HH Model Flop/Byte : 6.02
Level 1
Level 2
Results published in HiCOMB’10, Journal of Supercomputing, & Concurrency and Computation
HH model Speedup for different Architectures
0
100
200
300
400
500
600
700
800
900
0 2 4 6
Neurons (millions)
Sp
ee
du
p
Fermi GPU, OpenCL
Fermi GPU, CUDA
Telsa 870, OpenCL
Telsa 870, CUDA
AMD GPU, OpenCL
Intel Xeon
AMD Opteron
IMB PS3
Exploring Multiple Levels of Heterogeneous Performance Modeling
Use Synchronous Iterative GPGPU Execution (SIGE) Model for Synchronous Iterative Algorithms (SIAs)Relevant Equations describing the SIGE Model
Texecution = ∑Tcomp. + ∑Tcomm.
Tcomp.= Tpre-process + Tpost-process + TCPU + TGPU
TGPU = TGPU-Kernel + TPCIE-Transfers
TPCIE-Transfers = Thost-to-device + Tdevice-to-host
Tcomm. = ∑Tnetwork-transactions
Initial validation of low-level abstraction model for GPGPU clusters
Regression-based performance prediction frameworkSIA case studies: Spiking Neural Network (SNN) modelsAchieved over 90% prediction accuracy
Synchronous Iterative GPGPU Execution (SIGE) model
Regression models for CPU/GPU computations using Algorithm FLOPS
and Bytes
Regression models for PCIE and Infiniband using
micro-benchmarks
Gene Co-Expression Network Construction
• Accelerating construction of gene co-expression networks, which analyze the relationships among thousands of genes
• Previous techniques were slow and use excessive disk space
• Our acceleration has allowed generation of hundreds of gene networks of multiple sizes and types (rice, yeast, and human) for in-depth analysis never before possible
• Future work with GPUs and other accelerators will provide additional performance gain and enable larger studies
18X Faster45X Faster
7X Smaller
Robust Facial Recognition with Highly-Parallel Architectures
Facial recognitio
n Needs:
• Parallel processing of
multiple algorith
ms to
improve accuracy
• Faster identifi
cation
FPGAs offer:
• Algorithm-specific
capable hardware
• Parallel processing
of multiple algorithms
The rapidly growing field of biometrics uses
physical features to perform identity authentication.
Facial recognition is the user’s most convenient
biometric but often suffers from poor performance,
especially in applications with wide image variation.
Several facial recognition algorithms have been
developed that can adapt to particular types of image variation, but no single
algorithm can provide robust identification.
FPGAs and GPUs provide the necessary parallelism to run
multiple algorithms simultaneously and fuse their
results together to enable accurate recognition.
Name: Walt Ligon
Focus Area: CSA
http://www.parl.clemson.edu/~walt
Research Area:
Parallel Computing, Parallel File Systems, Programming Environments
Current Projects:
Parallel Virtual File System (PVFS)
High End Computing I/O Simulator (HECIOS)
Relevant courses: ECE 851, 873, 329, 493 (MPI)
Name: Robert Schalkoff
Focus Area: CSA/IS
http://www.ece.clemson.edu/iaal/index.html
Research Area:
Soft Computing/Parallel Programming
Current Projects:
An algebraic framework for multi-class motion estimation
using unsupervised learning with GPU implementation
Relevant courses: ECE 856, ECE 855, ECE872, ECE 642, ECE 847
An algebraic framework for multi-class motion estimation using unsupervised learning with GPU implementation
Optical flow constraint equation (OFCE) is I
x* u + I
y* v + I
t = 0
Pixel locations that suffer aperture problem have rank-deficient system.
The min-norm solution of rank-deficient system leads to motion estimates with low confidence. High confidence is associated
with vectors that do not suffer aperture problem.
Motion vectors (u,v) are separated into two sets; one set of vectors (H
p) that
suffer aperture problem and another set of vectors (H
c) that do not.
Implementation with NVIDIA CUDA: Compute Unified Device Architecture
A BDC
A B DCTextureMemoryGlobal
Memory
Mutable Immutable
1Shared Memory
SPSPSPSPSPSPSPSP
SM 0
Shared Memory
SPSPSPSPSPSPSPSP
SM 0
2
Kernels for Motion Estimation:1. Gradients Gradients
2. Local Motion Local Motion 3. SOFM/NG SOFM/NG
Name: Haiying Shen
Focus Area: CSA
http://www.ces.clemson.edu/~shenh
Research Area:
Distributed computer systems and computer networks
Current Projects:
Leveraging Hierarchical DHTs and Social Networks for P2P Live Streaming
P2P File Storage and Sharing System for High-End Computing
Pervasive Data Sharing Over Heterogeneous Networks
File Replication and Consistency Maintenance in Pervasive Distributed Computing
Hybrid Wireless Networks
Self-organizing P2P-based File Storage System in HPC
Relevant courses: ECE 429/629, ECE 893
P2P Live Streaming/VoD
Preliminary results published in ICPP10, Infocom11, IEEE TPDS 11(Images captured from paper Flexible Divide-and-Conquer protocol for multi-view peer-to-peer live streaming, P2P’09)
Social network
channel cluster
n
channel cluster
A
channel cluster
DHTs (Channels)
B
C
Features:(1) Distributed Hash Table is constructed for content delivery to increase scalability, availability(2) Social network is used for accurate content recommendation and channel switch to reduce video delivery latency
• Internet-based video streaming applications attract millions of online viewers every day.
• The incredible growth of viewers and dynamics of participants have posed a high quality-of-service (QoS) requirement.
• Goals: high scalability, availability, low-latency.
Pic from http://www.fmsasg.com/SocialNetworkAnalysis/
GENI Experiments on P2P, MANET, WSN Networks
Data sharing in P2P networks (Cycloid P2P)
Features:(1) Constant maintenance overhead regardless of the system scale.(2) Scalability, reliability, dynamism-resilience, self-organizing.
Number of nodes:
100
Dimension: 6
Node failure rate:
0.1-1 natural
Lookup/Insert interval
10-100s to every node
Total lookups 10000
Spatial-temporal similarity data sharing (SDS) in WSNs
Locality-based distributed data sharing protocol (LORD) in MANETs
Features:(1) Energy-efficient & scalable.(2) Reliable & dynamism-resilient.(3) Similarity search capability
Features:(1) Efficient spatial/temporal similarity data storage.(2) Fast query speed.(3) Low energy consumption.
Number of sensors 128
Node in zone 9
LSH destinations 5
Number of nodes (ORBIT) 100
Moving speed dist. (m/s) [0.5-2.5], [1-5], [20-30]
We will implement three existing data sharing algorithms on the P2P, MANET and WSN networks, thus identify and investigate potential issues in the data sharing applications in heterogeneous
networks.
Leveraging P2P in HPC/Cloud Computing
P2P network is well-known for scalability, reliability and self-organizing
Social network based P2P overlay construction (under review of INFOCOM12 )
Locality aware P2P overlay construction (CCNC 09)
Interest aware P2P overlay construction (CCGRID 09)
User behavior pattern aware P2P overlay construction (In preparation for IPDPS 12)
P2P-based Resource Management Effective and efficient P2P content delivery
algorithm design (TC11, TPDS10, INFOCOM11, IPDPS08)
P2P-based Reputation Management Social network Collusion detection (IPDPS11) Spam filtering (INFOCOM11) Game theory based cooperation incentive
analysis (ICCCN09, TMC)
P2P-based File Storage System in HPC File replication (JPDC09) File consistency maintenance (TPDS11)
Cloud computing
Grid computing
Pic from http://innovationsimple.com/web-hosting/cloud-hosting-web-hosting/benefits-of-cloud-computing/
Name: Darren Dawson
Focus Area: IS
http://www.ece.clemson.edu/crb/welcome.htm
Research Area:
Nonlinear Control and Estimation for Mechatronic Systems
Current Projects:
Following 3 Slides
Relevant courses: ECE 874, 801
Visual Servoing of Robot Manipulators
Problem: Control of Moving Objects in an Unstructured Environment is Difficult due to the Corrupting Influences of Camera Calibration with regard to Task Planning
Solution: Close the Control Loop with
Camera Measurements
Testbed Features a High-Speed
Real-Time Camera System
2.5D Visual Servoing
Design a Controller to Regulate
the Position and Orientation
of the End-Effector
Control Strategy Uses Both 2D
Image-Space and 3D Task-Space
Information
Next Generation Hardware-in-the-Loop Ground Vehicle Steering Simulator
Custom Honda CRV steering simulator with electric servo-motors
Test platform supports development of advanced ground vehicle steering technology using concepts from “robotics” field
Also examining in-vehicle operator feedback channels
• Visual (scene, lights)
• Haptic (steering wheel, …)
• Audio (tones/chimes/voice)
Human subject testing
Advanced Automotive Thermal Management Systems - Smart Components
Goal is to improve the engine’s cooling/heating system operation using mechatronic technology
• Improved fuel economy
• Reduced tailpipe emissions
• Flexible thermal system design
• Enhanced control of engine temperatures
Replace mechanical cooling system equipment with electric/hydraulic-driven components
Develop mathematical thermal models
Name: Tim Burg
Focus Area: IS
http://www.clemson.edu/~tburg
Research Area:
Nonlinear Control Applications
Current Projects:
Unmanned Aerial Vehicles
Biofabrication
Haptics
Environmental Monitoring
Relevant courses: ECE 874, 801
Bioprinting
Bioprinting - an approach to tissue engineering
Cells are precisely placed in a 3D structure using inkjet printer technology.
Active collaboration with Bioengineering.
ECE research focused on system integration, modeling, and control.
Haptics
Objective Is to identify, demonstrate, and quantify the potential benefits of specialized haptic user interfaces within a collaborative environment.
Name: Stan Birchfield
Focus Area: IS
http://www.ces.clemson.edu/~stb
Research Area:
Computer Vision
Current Projects:
Vision-based mobile robot navigation
Vehicle traffic monitoring
Robotic laundry handling
Relevant courses: ECE 847, 877, 904
Vision-Based Mobile Robot Navigation
Mobile robot equipped with single, off-the-shelf inexpensive camera
Developing algorithms for Traversing a known path by comparing the coordinates of tracked feature points
Detecting doors in indoor environments for navigation
Following a person moving about the environment, maintaining a desired distance
Applications: courier robots, tour guides, physician assistance
Vehicle Traffic Monitoring Using Cameras Developing algorithms for detecting,
tracking, and classifying vehicles automatically using video
Low-angle cameras cause occlusion and spillover
Shadows, reflections, and environmental conditions are addressed using a combination of feature tracking and pattern detection
Applications: intelligent transportation systems (ITS)
incident detection and emergency response
data collection for transportation engineering applications
Adam HooverFocus Area: IS/CSA
http://www.ces.clemson.edu/~ahoover/
Research Area: Tracking systems, embedded systems
Current projects:See the next 2 slides
Relevant courses: ECE 854, 668
Bite Counter
Automatically tracks how many bites of food have been taken
Worn like a watch
Bite count vs calories for 54 meals
1 in 3 Americans is obese, another 1 in 3 is overweight; worldwide there are more
overweight than underfed people
• 2011-2012 large cafeteria experiment in main campus dining hall• Equipment and software for recording and correlating video, scale, gyroscope data• Signal analysis to improve bite detection accuracy and bite:calorie correlation
Ultrawideband Position Tracking
Trilateration measures distances from a set of transmitters to a receiver to calculate position.
same idea
• Ubisense system in Riggs basement• Particle filter methods to improve accuracy• Noise modeling, combination with other sensors
and other sources of information such as maps
Richard Groff Focus Area: IS
http://www.ces.clemson.edu/~regroff
Research: Robotics and control applications at small length
scalesComputational and Experimental Tissue ModelingBiomimetics
Current Projects: Synthetic butterfly proboscises Biofabrication and Tissue Modeling (under revision)
Relevant coursework: ECE801 (linear systems), ECE847 (digital signal
processing)for some projects, some background in
magnetostatics, solid mechanics, materials science, and/or molecular biology desired
Synthetic Butterfly Proboscis
Butterflies can drink fluids of widely varying viscosities by controlling the shape of their feeding tube (probosicis)
Using custom fibers from Materials Science Department, generate a synthetic proboscis that can sample widely varying fluids
Proboscis
Fibers are paramagnetic or piezoelectric Control fiber shape using magnetic or electric
fields Preliminary work on modeling and position control
of magnetic microfibers
Experimental Platform for Magnetic
Microfibers
Tissue Engineering via Biofabrication
Biofabrication – develop a system to place living cells in 3D patterns mimicking native tissue many subprojects
Develop computational model for interaction of tumor cells and epithelial stem cells
Fluorescent-dyed murine D1 mesenchymal stem cells (red) and murine mammary cancer cells (red)
“Tissue Description Language” Specify Describe initial condition for
computational model Specify structure for biofabrication
Use TDL to study systems biology problems in cancer. (Feedback via intercellular signalling)
Name: Ian Walker
Focus Area: IS/CSA
http://www.ces.clemson.edu/~ianw/
Research Area:
Robotics
Current Projects:
Trunk and tentacle robots
Intelligent Robotic Workstations
Relevant courses: ECE 655, 868, 869
Invertebrate’ robot trunks/tentacles
Animated Architecture
Integrate Robotics and Architecture
Goal “Animated Work Environment”
What should you do next?
Find out more about specific research projectsweb, senior graduate students, faculty
Contact potential advisors about projects, openingsfaculty attending this meeting may be recruiting currently
Eithera) Mutually agree on advising relationship
ORb) Establish criteria for being evaluated/considered
ORc) Seek another advisor/project
Q & A
?