Transcript Design - Sequence Diagrams

Computer Engineering
Senior Projects
&
Research Overview
An informal overview of past & current
projects
students & my own
by
Al Davis
School of Computing
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The Engineering Discipline

Role
– design and build things
– change the world around us
» hopefully for the better
» hence faced with a continuous ethical dilemma

Ultimate requirement
– what we build must work

Requisite skills
– science: math, physics, chemistry, materials, CS, …
– engineering: state of the art, current practice, technology trends,
manufacturing, testability, maintenance, life cycle costs, …
– art: creative component that is clearly evident in the great
engineers
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Computer Engineering

Design and build computer systems
– inherently involves both software and hardware design skills

System software
– compiler, operating system, device drivers, …
– as opposed to application specific software
» applications are the target system “user”
» hence they are used in design evaluation (pre- and post-build)

Hardware: possibly many disciplines and levels
– VLSI chip design: analog and digital circuit aspects
» CS, EE, physics are the key disciplines
» yet cooling is a big issue – enter ME aspects
– board design: CS, EE, and manufacturing issues are dominant
– system design: balance of HW and SW capabilities
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CE Senior Projects at Utah

Logistics
– CE program run jointly by SoC and ECE departments
– Senior project is capstone project course
» team based
» students choose their own project
» best mechanism to demonstrate your abilities to future
employers
– CE Senior Project is a year long activity
» at least for the last 2.5 years
» Spring term of junior year: plan and propose
» Summer: get parts and start building (optional)
» Fall term of senior year: build and demonstrate
– Exit interview feedback
» rave reviews for being hard, fun, and instructive
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04 Projects


Satellite Tracking station
Weaver – a 802.11 remote control vehicle interface
– camera on car: image and commands to base station via wireless
– car has autonomous anti-collision capability (infrared)

GPS Hummer
– autonomous navigation and anti-collision
– some AI in route finding since Hummer remembers obstacles that it saw
previously

PCI Coprocessor
– efficient acceleration via PCI add-on

Jiggawax
– build your own iPod

RVI – remote vehicle interface
– control via web or cell phone
– control windows, engine, and door locks from RF base station
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05 Projects

Carputer
– OBDII car data and 802.11g auto-sync to base station
– monitor your car or your kids

IR tag
– paintball without the mess

Athlete monitor system
– real time tracking of position and heart rate to central coaching
station
– GPS, RF, and HRM on-athlete


Inverted pendulum 2-wheeled robot
Multi-carrier reflectometry
– finding faults in aircraft wires without tearing the plane apart

Glider avionics package
– using accelerometers, GPS, and strain sensors
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Current 06 projects (underway now)

PEN
– electronic paper – the only paper you’ll ever buy!

Recipedia
– a cook book that talks and listens to you

GPS tracker
– use campus ubiquitous wireless to keep track of where things are via your
cell phone or computer

OmegaCore
– a DVR that knows how to remove commercials for you

NoCPR
– bathtub drowning prevention

Tracking Visor
– virtual reality on your head
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Selected Examples

Some images to illustrate previous projects
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Satellite Tracking Station
Final dual band antenna
on the roof of MEB during
demo day
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2 meter (VHF side) antenna specs – students used an antenna design CAD tool
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GPS Hummer
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Controlling direction and speed with transistors
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GPS internals
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A build your own GPS kit from Motorola
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Autonomous anit-collision
system
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Glider Avionics Package (note this ended up being done by a single student as a thesis)
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Designing an electronic compass is non-trivial
especially if you want tilt-compensation
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Board
Schematic
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Power Supply
Filters and Registers
Board Artwork
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Senior Project Synopsis


This was just a peek
Just remember
– if you can imagine it you can usually build it
» there are some things you just can’t do
» like a perpetual motion machine

which violates the laws of physics
– all it takes is dedication and time


Huge diversity of both opportunities and problems
You might have noticed the world isn’t perfect
– so help fix it!
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Personal Research Overview

Past
– dataflow, VLSI, asynchronous circuits, parallel computing, high
performance architectures (50% academia, 50% industry)

Currently there are 4 projects
– Domain specific architectures
» target highly constrained embedded systems
» will highlight the perception processor today

have also worked in signal processing and cell phone domains
– Interconnect driven architecture
» w/ Rajeev Balasubramonian & students
– RPU design
» w/ Erik Brunvand, Pete Shirley, Steve Parker, & students
– VLSI wire scaling theory
» w/ Stephanie Forrest & Melanie Moses @ UNM
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Embedded Computing
Characteristics

Historically
–
–

narrow application specific focus
typically cheap, low-power, provide just enough compute
power
» niche filled by small microcontroller/dsp devices
» AND often ASIC component(s)
New Pressures
–
–
–
world goes bonkers on mobility and the web
» expects ubiquitous information access
» expects better and cheaper everything
sensors, microphones & cameras become free
» so use lots of them
now we’re talking real computing
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New Look for ECS

Sophisticated application suites
–
not single algorithms – e.g.
» 3G and 4G cellular handsets


»
process what is streaming in from the net

»


includes real time media & web access
process the sensor, microphone, and camera streams

–
multiple channels and multiple encoding models
plus the usual DSP stuff
plus network information from the neighborhood
since things are starting to happen in groups
wide range of services
» dynamic selection
»  no single app will do
Rate of algorithmic change is staggering
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ECS Economics

Traditional reliance on the ASIC design cycle
–
–
lengthy IC design - > 1 year typical
little re-use
»
IP import works but there are many pitfalls


–

turning an IC is costly
»
even when it works the first time
ECS product cycles
–
–

HDL code  synthesize  ed inefficiency
Macroblock  forces process and layout issues
lifetime similar to a mayfly
need next improved version “real soon now”
Result
–
sell monster volumes in a short time or lose
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What is Perception Processing ?


Ubiquitous computing needs natural human interfaces
Processor support for perceptual applications
–
–
–
–

Gesture recognition
Object detection, recognition, tracking
Speech recognition
Biometrics
Applications
–
–
–
–
Multi-modal human friendly interfaces (our focus)
Intelligent digital assistants
Robotics, unmanned vehicles
Perception prosthetics
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Perception Processing Problem
consider always on aspect!!
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Current Processors Inadequate



Too slow, too much power for embedded space!
–
2.4 GHz Pentium 4 ~ 60 Watts
–
400 MHz Xscale ~ 800 mW
–
10x or more difference in performance but 100x in power
Inadequate memory bandwidth
–
Sphinx requires 1.2 GB/s memory bandwidth
–
Xscale delivers 64 MB/s ~ 1/19th
Our methodology
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Characterize applications to find the problems
–
Derive acceleration architecture
»
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History of FPUs is an analogy
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The Problem w/ GPP’s

caches & speculation
–
–

rigid communication model
–
–
–

consume significant area and energy
great when they work – a liability when they don’t
data moves from memory to registers
register  execution unit  register
inability to support specialized computational pipelines
» ASIC advantage
bottom line
–
–
–
can process anything
but not efficiently in many cases
it’s the von Neumann trap
» lots of overhead for almost no work
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The FaceRec Application
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FaceRec In Action
Bobby Evans
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Application Structure
ANN based
Flesh tone
Image
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



Segment
Image
Rowley
Face
Detector
Viola &
Jones
Face
Detector
Neural
Net Eye
Locator
Eigenfaces
Face
Recognizer
~200 stage
Identity,
Adaboost
Coordinates
Flesh toning: Soriano et al, Bertran et al
Segmentation: Text book approach
Rowley detector, voter: Henry Rowley, CMU
Viola & Jones’ detector: Published algorithm + Carbonetto, UBC
Eigenfaces: Re-implementation by Colorado State University
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Application Profile
Eigen
Faces
19%
Eye
Locator
17%
Flesh tone
4%
Eigen
Faces
19%
Viola/
Jones
Detector
60%
Execution time break down
(Using Viola/Jones detector)
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Eye
Locator
10%
Flesh
tone
6%
Rowley
Detector
65%
Execution time break down
(Using Rowley's detector)
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Face Recognition Analysis

Cache
– small L1D$  high hit rate
– L2$ is useless – most L1 misses pass through

IPC
– low even with lots of FP execution units
– Why?
» load store register & memory ports saturate


multiple large matrix traversals are the critical kernel
several indirect accesses per operation
» dominant loop is a SFP inner product


no single cycle accumulate
Implications
– restructure the code – loop fusion  more temporary reg’s
– need architectures which move data well
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CMU Sphinx 3.2 Profile
Feature Vector = 13 Mel + 1st and 2nd derivative
10 ms of speech is compressed into 39 SP floats
iMic possibility
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Speech Analysis

Results
– similar to FaceRec
» cache
» port saturation
– big difference
» also memory B/W starved
» due to language model
FE
0.98%
HMM
41.45%
GAU
57.57%
Execution time
(opt)
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Simple ASIC Design Example:
Matrix Multiply
def matrix_multiply(A, B, C): # C is the result matrix
for i in range(0, 16):
for j in range(0, 16):
C[i][j] = inner_product(A, B, i, j)
def inner_product(A, B, row, col):
sum = 0.0
for i in range(0,16):
sum = sum + A[row][i] * B[i][col]
return sum
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ASIC Accelerator Design:
Matrix Multiply
Control Pattern
def matrix_multiply(A, B, C): # C is the result matrix
for i in range(0, 16):
for j in range(0, 16):
C[i][j] = inner_product(A, B, i, j)
def inner_product(A, B, row, col):
sum = 0.0
for i in range(0,16):
sum = sum + A[row][i] * B[i][col]
return sum
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ASIC Accelerator Design:
Matrix Multiply
Access Pattern
def matrix_multiply(A, B, C): # C is the result matrix
for i in range(0, 16):
for j in range(0, 16):
C[i][j] = inner_product(A, B, i, j)
def inner_product(A, B, row, col):
sum = 0.0
for i in range(0,16):
sum = sum + A[row][i] * B[i][col]
return sum
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ASIC Accelerator Design:
Matrix Multiply
Compute Pattern
def matrix_multiply(A, B, C): # C is the result matrix
for i in range(0, 16):
for j in range(0, 16):
C[i][j] = inner_product(A, B, i, j)
def inner_product(A, B, row, col):
sum = 0.0
for i in range(0,16):
=
sum
sum
return sum
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+
A[row][i]
*
B[i][col]
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ASIC Accelerator Design: Matrix Multiply
def matrix_multiply(A, B, C): # C is the result
matrix
for i in range(0, 16):
for j in range(0, 16):
C[i][j] = inner_product(A, B, i, j)
def inner_product(A, B, row, col):
sum = 0.0
for i in range(0,16):
sum = sum + A[row][i] * B[i][col]
return sum
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How can we generalize ?

Decompose loop into:
– Control pattern
– Access pattern
– Compute pattern
Programmable h/w acceleration for each pattern
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Architecture Family
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Experimental Method


Measure processor power on
–
2.4 GHz Pentium 4, 0.13u process
–
400 MHz XScale, 0.18u process
Perception Processor
–
1 GHz, 0.13u process (Berkeley Predictive Tech Model)
–
Verilog, MCL HDLs
–
Synthesized using Synopsys Design Compiler
–
Fanout based heuristic wire loads
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Spice (Nanosim) simulation yields current waveform
–
Numerical integration to calculate energy

ASICs in 0.25u process

Normalize 0.18u, 0.25u energy and delay numbers
–
model = constant field scaling
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Benchmarks

Visual feature recognition
–
–
–

Speech recognition
–
–

HMM: 5 state Hidden Markov Model
GAU: 39 element, 8 mixture Gaussian
DSP
–
–

Erode, Dilate: Image segmentation operators
Fleshtone: NCC flesh tone detector
Viola, Rowley: Face detectors
FFT: 128 point, complex to complex, floating point
FIR: 32 tap, integer
Encryption
–
Rijndael: 128 bit key, 576 byte packets
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Results: IPC
Mean IPC =
3.3x R14K
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Results: Throughput
Mean
Throughput =
1.75x Pentium
0.41x ASIC
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Results: Energy
Mean
Energy/packet =
7.4% of XScale
5x of ASIC
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Results: Energy Delay Product
Mean EDP =
159x XScale
1/12 of ASIC
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Perception Results: Summary





41% of ASIC’s performance
But programmable!
1.75 times the Pentium 4’s throughput
But 7.4% of the energy of an XScale!
 advanced perceptive embedded systems are
possible
–
–

above results are maximally pessimistic
and as always there are improvements in the works
Problems
–
–
–
manually intensive design process
requires highly skilled programmer, architect, circuit
designer
current effort is to fix this
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Automating the design process
Application Suite
C
Host Code
C & ifc
Splitter
Human
opt. Stream Code
Interaction
Stream
Compiler
Host
Compiler
CoProcessor
Description
Host
Object Code
Synthesize
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design
choice
CoProcessor
Simulator
CoProcessor
Object Code
dilation
Design Track
Graph
add point
Simulation Analysis
&
Design Space Explore
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DSE Results
Power
Performance Requirement
No Way Quadrant
Too “Watty”
Quadrant
Power Limit
Too Dweeby Quadrant
Choice Quadrant
Performance
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Conclusions

Significant benefit
–
–

3 forms of parallelism: control, address, execution
program controlled communication patterns
» able to mimic ASIC flows
» more efficient use of execution units and memory
structures
Results to date (in terms of ed)
–
–
–
2-3 orders of magnitude improvement over GPP
within 1 order of magnitude of an ASIC
while maintaining most of the generality of the GPP
approach
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Thanks!
Questions?
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