Processor Architecture Needed to handle FFT algoarithm - PowerPoint PPT Presentation

Title: Processor Architecture Needed to handle FFT algoarithm


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Processor Architecture Needed to handleFFT
algoarithm

• M. Smith

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FFT algorithms

• There are more FFT algorithms performed per day
  than any other algorithm in the world
• Therefore, of course, custom parts of the
  processors to handle this situations

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1 MRI session
http//www.core.org.cn/NR/rdonlyres/Nuclear-Engine
ering/22-56JFall-2005/EA28B7B3-39E5-4999-B858-5E3B
248E5408/0/chp_mri.jpg
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Magnetic resonance (MR) and DFT discrete Fourier
transform

• Place a body in a steady magnetic field 1.5
  Telsa, (3 or 7)
• All the protons spin (precess) around magnetic
  field at a frequency of around10 MHz
• If sent in an RF (90 degree) pulse at 10 MHz
  will be absorbed by system.
• Some energy then emitted by system as sinusoid at
  10 MHZ
• Do DFT single pulse whose height proportional
  to number of protons in body (amount of hydrogen
  in body amount of water in body)
• Used to non-destructively measuring water content
  in wheat

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Magnetic resonance imaging (MRI) and DFT
discrete Fourier transform

• Place four bodies in a steady magnetic field
  1.5 Telsa, (3 or 7)
• Apply 90 pulse
• Apply DFT on response
• One signal at 10 MHz
• Place four bodies in a steady magnetic field of
  1.5 Telsa
• Apply 90 pulse
• Apply a field gradient in X direction
• Apply DFT on response
• Four signals 10 G.X1, 10 G.X2, 10 G.X3 , 10
  G.X4 where Xi is the x position of object

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Magnetic resonance imaging (MRI) and DFT
discrete Fourier transform

• Place four bodies in a steady magnetic field of
  1.5 Telsa
• Apply 90 pulse
• Apply a field gradient Gx in X direction
• Apply DFT on response
• Four signals 10 Gx.X1, 10 Gx.X2, 10 Gx.X3 ,
  10 Gx.X4 where Xi is the x position of object
• Apply 90 pulse
• Now add a field gradient in both X and Y
  directions
• Apply DFT on response
• Four signals 10 Gx.X1 Gy.Y1, 10 Gx.X2
  Gy.Y2, 10 Gx.X3 Gy.Y3, 10 Gx.X4 Gy.Y3
  where Xi is the x position of object and Yi is
  the i position of object

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1 MRI session
http//www.core.org.cn/NR/rdonlyres/Nuclear-Engine
ering/22-56JFall-2005/EA28B7B3-39E5-4999-B858-5E3B
248E5408/0/chp_mri.jpg

• Occurs for about 20 minutes
• Echo planar imaging (EPI) Generates 19 2 D slices
  of the brain in about 60 seconds
• Each image is 256 pixels by 256 pixels x 19
• Each image requires 256 256 19 DFTs / minute
• DFT is only ONE part of the algorithm
• My research is using MRI for stroke diagnosis

http//www.magnet.fsu.edu/education/tutorials/magn
etacademy/mri/images/mri-scanner.jpg
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Tackled already this term

• Three types of DSP algorithms
• Long loops, multiplication and addition
  intensive, regular (simple) memory accesses
  e.g. 300 taps in FIR algorithms
• Short loops involving multiplications and
  additions e.g. 3 stages in IIR algorithms

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Comparing IIR and FIR filters
Infinite Impulse Responsefilters few
operations to produce output frominput for each
IIR stage 3 7 stages
Finite Impulse Responsefilters many operations
to produce output frominput. Long FIFO buffer
whichmay require as many operations As FIR
calculation itself. Easy to optimize
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Discrete Fourier Transform

• FIR and IIR algorithms directly manipulate the
  data in the time domain.
• FIR -- Process M data points using N point FIR
  filter involves M (N-1) additions M
  N multiplications M N 2 M memory
  accesses Algorithm takes a time of Order (M
  N)
• Very slow if manipulating large amount of data

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Frequency domain analysis

• Apply discrete Fourier transform (implemented via
  FFT)
• Transform to frequency domain takes time Order (M
  log M)
• Perform FIR in frequency domain takes time Order
  (M)
• Transform back to time-domain takes time Order (M
  log M)
• FFT (Order (M log M) is orders of magnitude
  faster that FIR (Order (M log M)

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4 point DFT to show concepts
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Simplify using special complex exponential
properties
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Running FFT on data stored in array
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8 point FFT with log 8 ( 3) stages

• 3 stages with N / 2 butterflies / stageOrder
  (N log N) in time

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Architectural characteristics needed to handle
FFT efficiently
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Add / subtract in one instruction

• The following instruction is illegal as a single
  instruction
• F4 F2 F3, F5 F6 F7 ILLEGAL
• Needs bits to describe 6 registers (6 4 bits)
• FFT Butterfly add is special instruction
• F4 F11 F12, F5 F11 F12
• Uses only 4 registers, 2 in, 2 out (4 2 bits)
• 2 bits how come?
• F4 F12 F11, F5 F12 F11 ILLEGAL
• Fx F11-8 F15-12, Fy F11-8 - F15-12

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Memory accesses

• Stage 1
• Fetch X data at location k and k N /2
• Store X data at location k and k N /2
• Stage 2
• Fetch X data at location k and k N /4
• Store X data at location k and k N /4
• Stage 3 -- Final stage
• Fetch X data at location k and k N /8
• Store X data at bit-reversed location k and k N
  /4

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First issue how do you store complex numbers?

• One option
• Use 16-bit values
• Store real part in top 16-bits
• Store imaginary part in bottom 16 bits
• Access data on J-bus
• Access complex sinusoids on J-bus
• Access both components (R and I) in one cycle
• TigerSHARC has the ability to do 16-bit complex
  additions and multiplications as specific
  instructions INTEGER only (NOT SHARC)
• Can use both X and Y compute blocks

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Integer operations a pain tend to overflow --
TigerSHARC syntax

• Option 2 floating point
• Store Real component in location X and imaginary
  component in location Y
• Use R10 QJ4 4
• Store first imaginary number in X0 and Y0
• Store second imaginary number in X1 and Y1
• FR3 R1 R0 performs complex floating point
  addition in single cycle
• LJ5 R3 stores complex answer back

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Integer operations a pain tend to overflow --
TigerSHARC syntax

• Option 3 floating point
• Access Real component along J- bus from data1
  and Imaginary component along K-bus from data
  2
• Use XR30 QJ4 4 YR30 QK4 4
• Store first imaginary number in X0 and Y0
• Store second, third and fourth imaginary number
  in XR1, YR1 XR2, YR2 XR3, YR3
• Which option is best? Depends? How handle bring
  in complex sinusoids

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Bit reverse addressing
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Bit reverse addressing Check manual for
accurate details before MII

• Only possible with I0, I1, I2? and I3? registers
  (also I8, I9, I10?, I11?)
• You must start the array on a N aligned boundary
  otherwise it does not work
• I0 address pointer
• B0 base register point to start of array
• L0 length of array register
• M0 special circular buffer modify register ????
• F4 BR I0 1 // Correct SHARC syntax???
• Bit-reverse addressing only works on POST-MODIFY
  (permits next address to be calculated in
  parallel)

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Issues handling FFT Butterfly
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Only possible on TigerSHARC
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Wrong again

• This is using the Radix 2 form of the algorithm
  breaks down into 2-pt DFT
• There is also a Radix 4 form of the algorithm
  which is faster again

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TIGERSHARC
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DSP Co-processor on SHARC
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Will discuss FFT accelerator later

• Was not available on previous SHARCS
• Way to go with future processors
• Cheap processors co-processors
• Cheap microcontroller FPGA component