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ECE DepartmentRice University
TI/Rice Review
Agenda
8:55am Overview of TI/Rice Relationship
9:15am Faculty Roundtable (TI-LU)
10am Student Roundtable
11am Demos and Tour
Sidney Burrus
Richard Baraniuk
TI/Rice Relationship
• Rice DSP Group organized in late 1960s by Sidney Burrus and Thomas Parks
• Numerous DSP textbooks on filter design, FFTs, …– Parks/Jones TI DSP Laboratory Textbook
• Several DSP short courses at TI
• Culminated in TI’s 1996 gift of $7M in recognition of “Rice’s leadership in DSP”– TI Wing of Duncan Hall ($3M, completed in 1996)– TI Visiting Professorship ($1.5M endowed)– TI Fellowship Program ($2.5M for 10+ year term)
TI Visiting Professors
2009 Nick Laneman, Notre Dame+ potentially Yonina Eldar, Technion
2006 Ron DeVore, South Carolina2005 Youji Yamada, Ishikawa NCT, Japan
Per Mikael Käll, Chalmers, Sweden2002 Tom Parks, Cornell2002 Urbashi Mitra, USC2002 Sheila Hemami, Cornell2001 David Munson, Michigan2000 Mike Orchard, Princeton1998 Doug Jones, Illinois1997 Geoff Davis, Dartmouth
TI Fellows
• Regular and Distinguished TI Fellowships– first year, then top up in subsequent years
• Offered only to our most outstanding applicants– 5-10 per year
• Step function improvement in student quality
• Recent TI Fellows in academe (since 2005)– Rebecca Willett, Duke – Clay Scott, Michigan– Chris Rozell, Georgia Tech– Michael Wakin, Michigan– Justin Romberg, Georgia Tech– Vinay Ribeiro, IIT-Delhi– Mike Rabbat, McGill– Ricardo von Borries, UTEP
TI Leadership University
• Research on emerging DSP and related technologies• In cooperation with MIT and Georgia Tech• Funding $1M / 3 years
1998-2001 Advanced DSP Theory and Algorithms, Wireless, Networking, DSP Laboratory
2002-2004 Advanced DSP Theory and Algorithms, Image Processing, Network Applications, Power Aware Wireless Communications
2005-2007 TI-LU Innovation Fund competition (7 projects)
2008-2010 DSP-enabled Sensors; DSP for 4G and Beyond, Mesh networks, Ad Hoc Networks
Connexions
• non-profit open education publishing project
• goal: make high-quality educational content available to anyone, anywhere, anytime for free on the web and at very low cost in print
• open-licensed repository of Lego-block modules that comprise courses/collections
• open-source tools enable authors, instructors, and learners to create, rip, mix, burn modules and courses
• Creative Commons open-content licenses, XML tools
• Partners: TI, IEEE, NI, Hewlett Foundation, …
Connexions
stanfordillinois
michiganwisconsinberkeley
ohio statega tech
uteprice
cambridgenorway
italy
CNX/TI
Agenda
8:55am Overview of TI/Rice Relationship
9:15am Faculty Roundtable (TI-LU)
10am Student Roundtable
11am Demos and Tour
Richard Baraniuk
Traditional Digital Data Acquisition
compress transmit/store
receive decompress
sample
sparse /compressiblewavelettransform
Nyquist rate samples
JPEGJPEG2000
What’s Wrong with this Picture?
Q: Why go to all the work to acquire N samples only to discard all but K pieces of data?
compress transmit/store
receive decompress
sample
sparse /compressiblewavelettransform
Compressive Sensing (CS)• Directly acquire “compressed” data
– replace samples with more general (random) “measurements”– equivalent to sub-Nyquist sampling
• Recover signal by convex optimization• Enables design of new cameras, imaging algs, ADCs,
sensor arrays and networks, …
compressive sensing transmit/store
receive reconstruct
Kevin Kelly
CS Camera Research Vision
• New modalities– IR– Hyperspectral– THz, gamma ray, x ray
• New geometries– Microscopes (confocal)– 3D cameras– Distributed camera arrays
• New algorithms– Graphical model based reconstruction and processing– 3D object/scene reconstruction– Computer vision applications– Joint articulation manifold (JAM) processing
DLP-CS Camera
single photon detector
DMD
VisIR
IR
Vis
Dual Visible/Infrared Imaging
CS Hyperspectral Imaging
DMD
PMTCAM
SpecimenPlane
Collimating Lens
Excitation Filter
AlignmentMirror #1
AlignmentMirror #2
DichroicMirror
DMD
ObjectiveLens
Imaging Lens(f160)
Relay Telescope
RotatedMirror
RotatedMirror Emission
Filter
CollectionLens
Eyepiece
MicromirrorsOFF OFF OFF OFFON ONON
Illumination Beam (from light source)
In-Focus Fluorescence (to Detector)
Excitation Beam (to Object)
Emission Beam
Out-of-Focus Fluorescence
Discarded Illumination
Raster
16x16 32x32
CS
32x32 128x12810:1
CS 256x256, 2:1
DLP-CS Microscopy
Michael Orchard
Location-Based Image RepresentationProf. Michael T. Orchard
•Project Theme -Images carry two types of information: a) the Brightness of things
and b) the Location of things•Objective -To develop a method for representing images that naturally and efficiently specifies both types of information in a unified framework.
•Approach -Complex filter banks: Magnitudes represent Brightness
Phases representLocation•Applications -Image and Video Coding; Image Denoising; Inpainting; Video Super-resolution
•Examples -Image Inpainting (work with Gang Hua - currently at TI)
Conclusion
• Three top open issues in image representation:
Location!
Location!
Location!
Farinaz Koushanfar
Example 1:Single transistorvariations [Roy and Asenov, Science, 2005]
Example 2: Invasive measurements of spatial variations on a 130nm die[Friedberg et al., ISQED, 2005]
Introduction• Fast non-invasive chip tomography: rapid non-intrusive
characterization of the spatial distribution of silicon variability for complex integrated circuits
• Motivation– Miniaturization of CMOS complex chips with billions of gates– CMOS patterns and atomic doping are uncontrollable
inherent statistical inter-die and intra-die variations– Variations are not completely random there is a spatial
correlation – The characteristics of each IC are unclonable and unique– Many analytical models are proposed, but post-silicon
characterization is missing – Post-silicon gate characterization and tomography of intrinsically
opaque ICs paves the way for many new applications• Applications range from security and hardware malware
detection, to more accurate analysis models and fast simulations
Tomography by compressive sensing• Once the sparse basis is found, random projections onto incoherent
basis will not be sparse/compressible– Random projections are universally incoherent– Fewer test measurements– No sparsity location information– Construction via optimization– Highly asymmetrical (most measurements and computing are off chip)
Example:[Baraniuk IEEE SP’07]
: random Gaussian measurement matrix: discrete cosine transform s: coefficient vector (sparse with K=4)
=: is measurement matrix with 4 columns corresponding to the nonzero si’sy: measurements (linear combinations of 4)
Tomography
FastTomography
Test input vector x (N)
N measurements
Gate-levelModels
Density estimation – Tomography (k sparse)
Linear Eq. system
Test input vector x (K)
K << N measurements
Density estimation – Tomography (k sparse)
L1 norm optimization
• Important new direction: use the post-silicon tomography information to determine the best placement of additional sensors to maximize controllability and observability to the ICs internals
Edward Knightly
Ed Knightly
Challenge: Ultra-Low Cost Wireless Networking for Under-Served Communities
Wireless ISP for Houston’s East End since late 2004 Over 4,000 users in 3 square kilometers Research platform: programmable and observable Multi-tier architecture
Ed Knightly
Research Challenges
Increased capacity: Multi-* MAC protocols
High resilience: exploit network structure
Proof-of-concept implementation
Advanced services and applications– Quality of service for voice– Mobile multimedia– Health sensing– Location services
Understanding community IT needs to drive services– Collaboration with Jerome Crowder (UH) and ethnographic studies
Behnaam Aazhang
“Opportunistic and Cooperative” Physical Layer
• Layers operate on different timescales– Session– Packet– Bit– Signal
• Coherence– Channels– Network
S D
R
Data
Coherent combining
Research Agenda
• Network state information– Topology– Location
• Network discovery– Distributed– Network flooding
• Cooperative routing
2 8
1
3
5 7
D
S
6
4
Joseph Cavallaro
CMC Research Lab
• Wireless Systems– Communication theory– VLSI architecture– Networking
Research Partners: NSF, State of Texas, Nokia, Xilinx, Texas Instruments, National Instruments
Processors in Future Wireless Systems
• Architecture Tradeoffs– ASIC Efficiency and Customization– DSP Flexibility and Configurability
• Special Function Units (SFUs)
• ASIPs (Application Specific Instruction set Processors): – Flexible and Retargetable Compilation– Identify and Exploit Algorithm Parallelism
Special-Purpose Architectures for 4G Systems
• MIMO Detection – Sphere Detection– Iterative Algorithms
• Detection-Decoding
• Channel Decoding– Low Density Parity Check Codes– Flexible, Scalable Architecture– Adapts to Code Rates– Data rates up to 1Gbps
Ashutosh Sabharwal
Wireless open-Access Research Platform (WARP)
• Custom hardware: radio daughtercards
• Research PHY and MAC
WARP System Demo in TI DSP Elite Lab
• Cognitive Radios, Distributed Wireless Networks and Testbeds
http://youtube.com/profile_videos?user=ricewarp
Lin Zhong
Efficiency-driven wireless communication Efficiency-driven wireless communication
Performance-driven design Efficiency only at peak performance
Objective: Adaptively guarantee efficiency across a broad range of data rates Advancement in RF design provides low overhead power-saving modes
Physical layer Coding/modulation for improved hardware activity patterns
MAC layer (in the context of 802.11, WARP platform) Use of directional antenna (up to 60% power reduction) Micro power management: idle periods during active data transfers
(up to 30% power reduction)41
Data rate
Energy per bit transceiving
Distribution of real usage
Sensitive computing How can computers serve without active
user engagement? Mobile phones spend most time idle waiting Bottleneck is their capability to “sense” their physical
environment, including users
Sense of a modern mobile device RF interfaces, camera, microphone, sensors
(e.g. accelerometer) Efficient gathering of the data Inference based efficient DSP User-friendly application of the sense
42
Agenda
8:55am Overview of TI/Rice Relationship
9:15am Faculty Roundtable (TI-LU)
10am Student Roundtable
11am Demos and Tour
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