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

?