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Is F Better than D

David Hansen and James Michelussi. Is F Better than D. Introduction. Discrete Fourier Transform (DFT) Fast Fourier Transform (FFT) FFT Algorithm – Applying the Mathematics Implementations of DFT and FFT Hardware Benchmarks Conclusion. DFT.

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Is F Better than D

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  1. David Hansen and James Michelussi Is F Better than D

  2. Introduction • Discrete Fourier Transform (DFT) • Fast Fourier Transform (FFT) • FFT Algorithm – Applying the Mathematics • Implementations of DFT and FFT • Hardware Benchmarks • Conclusion

  3. DFT • In 1807 introduced by Jean Baptiste Joseph Fourier. • allows a sampled or discrete signal that is periodic to be transformed from the time domain to the frequency domain • Correlation between the time domain signal and N cosine and N sine waves X(k) = DFT Frequency Signal N = Number of Sample Points X(n) = Time Domain Signal WN = Twiddle Factor

  4. DFT (Walking Speed) • Why is this important? Where is this used? • allows machines to calculate the frequency domain • allows for the convolution of signals by just multiplying them together • Used in digital spectral analysis for speech, imaging and pattern recognition as well as signal manipulation using filters • But the DFT requires N2 multiplications!

  5. FFT (Jet Speed) • J. W. Cooley and J. W. Tukey are given credit for bringing the FFT to the world in the 1960s • Simply an algorithm for more efficiently calculating the DFT • Takes advantage of symmetry and periodicity in the twiddle factors as well as uses a divide and conquer method • Symmetry: WNr +N/2 = -WNr • Periodicity: WNr+N = WNr • Requires only (N/2)log2(N) multiplications ! • Faster computation times • More precise results due to less round-off error

  6. FFT Algorithm • Several different types of FFT Algorithms (Radix-2, Radix-4, DIT & DIF) • Focus on Radix-2 using Decimation in Time (DIT) method • Breaks down the DFT calculation into a number of 2-point DFTs • Each 2-point DFT uses an operation called the Butterfly • These groups are then re-combined with another group of two and so on for log2(N) stages • Using the DIT method the input time domain points must be reordered using bit reversal

  7. Butterfly Operation

  8. Bit Reversal

  9. 8-Point Radix-2 FFT Example

  10. 8-Point Radix-2 FFT Example

  11. Implementations of DFT and FFT David Hansen

  12. DFT Implementation for (r=0; r<=samples/2; r++) { float re = 0.0f, im = 0.0f; float part = (float)r * -2.0f * PI / (float)samples; for (k=0; k<samples; k++) { float theta = part * (float)k; re += data_in[k] * cos(theta); im += data_in[k] * sin(theta); } } • Nested For Loop, (N/2)*N Iterations… O(N2) • 63027.41 Cycles / Sample (123 cycles per inner loop iteration) • Obvious Inefficiencies, cos and sin math.h functions • Efficient assembly coding could reduce the inner loop to 3 cycles per iteration (1,536 cycles / sample)

  13. C++ FFT Implementation void fft_float (unsigned NumSamples, float *RealIn, float *ImagIn, float *RealOut, float *ImagOut ) { for ( i=0; i < NumSamples; i++ ) { // Iterate over the samples and perform the bit-reversal j = ReverseBits ( i, NumBits ); } BlockEnd = 1; // Following loop iterates Log2(NumSamples) for ( BlockSize = 2; BlockSize <= NumSamples; BlockSize <<= 1 ) { // Perform Angle Calculations (Using math.h sin/cos) // Following 2 loops iterate over NumSamples/2 for ( i=0; i < NumSamples; i += BlockSize ) { for ( j=i, n=0; n < BlockEnd; j++, n++ ) { // Perform butterfly calculations } } BlockEnd = BlockSize; } }

  14. C++ FFT Implementation • Bit-Reverse For Loop – N iterations • Nested For Loops • First Outer Loop – Log2(N) iterations • Made use of sin/cos math.h functions • Second Outer Loop – N / BlockSize iterations • Inner Loop – BlockSize/2 iterations • O(N + Log2(N) * N/BlockSize * BlockSize/2) • O(N+N*Log2(N)) • 193.84 Cycles / Sample

  15. Assembly FFT Implementation • Bit-Reverse Address Generation • Hide Bit-Reverse operation inside first and second FFT Stages • Sin and Cos values stored in a Look-Up-Table • 256 Kbyte LUT added to Data1 • Needed to grow Data1 Memory Space using LDF file • Interleaved Real and Imaginary Arrays • Quad Reads Loads 2 Complex Points per Cycle • Supports the Real FFT for input signals with no Imaginary component • 40% Algorithm-based Savings

  16. Assembly FFT Implementation • Special Butterfly Instruction • Can perform addition/subtraction in parallel in one compute block • Speeds up the inner-most loop • VLIW and SIMD Operations • Performs simultaneous operations in both compute blocks • Loop unrolling and instruction scheduling keeps the entire processor busy with instructions. • 11.35 Cycles per Sample

  17. Assembly FFT Implementation

  18. DC FFT Test FFT Source Array FFT Output Magnitude

  19. Audio FFT Test FFT Source Array FFT Output Magnitude

  20. 1024 Point DFT / FFT Comparison

  21. 1024 Point Radix-2 FFT Hardware Comparison

  22. Conclusion • The FFT algorithm is very useful when computing the frequency domain on a DSP. • FFT is much faster than a regular DFT algorithm • FFT is more precise by having less errors created due to round off. • The timed coding examples further support this claim and demonstrate how to code the algorithm. • The Radix-2 FFT isn’t the fastest but it uses a less complex addressing and twiddle factor routine • In this case (unlike in school) F is better then D.

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