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

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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