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TI/Rice Review
ECE Department
Rice University
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
– TI Visiting Professorship
– TI Fellowship Program
($3M, completed in 1996)
($1.5M endowed)
($2.5M for 10+ year term)
TI Visiting Professors
2009
Nick Laneman, Notre Dame
+ potentially Yonina Eldar, Technion
2006
2005
Ron DeVore, South Carolina
Youji Yamada, Ishikawa NCT, Japan
Per Mikael Käll, Chalmers, Sweden
Tom Parks, Cornell
Urbashi Mitra, USC
Sheila Hemami, Cornell
David Munson, Michigan
Mike Orchard, Princeton
Doug Jones, Illinois
Geoff Davis, Dartmouth
2002
2002
2002
2001
2000
1998
1997
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
stanford
illinois
michigan
wisconsin
berkeley
ohio state
ga tech
utep
rice
cambridge
norway
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
Nyquist rate
samples
sample
JPEG
JPEG2000
transmit/store
compress
sparse /
compressible
wavelet
transform
receive
decompress
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?
sample
transmit/store
compress
sparse /
compressible
wavelet
transform
receive
decompress
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
receive
reconstruct
transmit/store
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
Dual Visible/Infrared Imaging
Vis
IR
DMD
DMD
Vis
CS Hyperspectral Imaging
IR
DLP-CS Microscopy
DMD
Dichroic
Mirror
Relay Telescope
Collection
Lens
Eyepiece
CAM
PMT
Rotated
Mirror
Rotated
Mirror
Emission
Filter
CS
Imaging Lens
(f160)
256x256, 2:1
Alignment
Mirror #1
Alignment
Mirror #2
Excitation Filter
Objective
Lens
Collimating Lens
Specimen
Plane
Micromirrors
Raster
OFF ON OFF ON ON OFF OFF
16x16
Illumination Beam
(from light source)
In-Focus
Fluorescence
(to Detector)
32x32
Discarded
Illumination
Out-of-Focus
Fluorescence
Emission Beam
Excitation Beam
(to Object)
CS
32x32
128x128
10:1
Michael Orchard
Location-Based Image Representation
Prof. 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:
•Applications -
Magnitudes represent
Phases
represent
Image and Video Coding; Image Denoising; Inpainting;
Video Super-resolution
•Examples Image Inpainting (work with Gang Hua - currently at TI)
Brightness
Location
Conclusion
• Three top open issues in image
representation:
Location!
Location!
Location!
Farinaz Koushanfar
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
Example 2:
Example 1:
post-silicon characterization
is missing
Invasive measurements
Single transistor
of spatial variations on
a
variations
– Post-silicon gate characterization
and
130nm die
[Roy and Asenov,
tomography
of
intrinsically
ICs paves
Science, 2005]
[Friedberg etopaque
al., ISQED, 2005]
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’s
y: measurements (linear combinations of 4)
Test input
vector x (N)
N
measurements
Linear Eq.
system
Fast
Test input
Tomography vector x (K)
K << N
measurements
L1 norm
optimization
Tomography
Gate-level
Models
Density estimation –
Tomography (k sparse)
Density estimation –
Tomography (k sparse)
• 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
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
Ed Knightly
Behnaam Aazhang
“Opportunistic and Cooperative”
Physical Layer
• Layers operate on different timescales
–
–
–
–
Session
Packet
Bit
Signal
• Coherence
– Channels
– Network
R
Data
S
Coherent combining
D
Research Agenda
• Network state information
– Topology
– Location
• Network discovery
– Distributed
– Network flooding
6
D
• Cooperative routing
7
5
1
4
S
2
3
8
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

Energy per bit transceiving
Efficiency only at peak performance
Distribution of real usage
Data rate

Objective: Adaptively guarantee efficiency across a broad
range of data rates


Physical layer


Advancement in RF design provides low overhead power-saving modes
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
41
to 30% power reduction)
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


Sensing
Energy
efficiency
Usability
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Agenda
8:55am
Overview of TI/Rice Relationship
9:15am
Faculty Roundtable (TI-LU)
10am
Student Roundtable
11am
Demos and Tour