1 / 12

Distributed Arithmetic to Digital Signal Processing: A Tutorial Review Stanley A. White, 1989

Literature Review. Distributed Arithmetic to Digital Signal Processing: A Tutorial Review Stanley A. White, 1989. Fengbo Ren fren@ee.ucla.edu. Oct. 7 th 2011. Distributed Arithmetic (DA).

tawana
Download Presentation

Distributed Arithmetic to Digital Signal Processing: A Tutorial Review Stanley A. White, 1989

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Literature Review Distributed Arithmetic to Digital Signal Processing: A Tutorial Review Stanley A. White, 1989 Fengbo Ren fren@ee.ucla.edu Oct. 7th 2011

  2. Distributed Arithmetic (DA) • DA is basically a bit-serial computational operation that forms an inner product of a pair of vectors, where one of the vector is preferred to be constant

  3. For Example

  4. Hardware Implementation • One-bit-at-a-time (1BAAT) fashion • When K>N, faster than mult.+accum. • Require 2K words of memory (huge resources) • Take N cycles to get results (Slow)

  5. Offset Binary Code (OBC) • Reduces 2K words to 2K-1 Ckn = {-1,1}

  6. OBC Implementation

  7. Vector Decomposition • Reduces 2K-1 words to P2M words (MxP=K-1)

  8. Multiple-Bit-at-A-Time • L-bit/clk/virable • Reduces computing time from N clk cycles to N/L

  9. The Best # of Parallelism • Relative Cost • When w=1 • L-BAATfor best performance/cost

  10. Application Example • Digital Filter

  11. Other Applications • Adaptive Filters • Coefficients can be time-varying • Circular convolution • Sinusoidal transform • Discrete Hartley transform (DHT) • Discrete Fourier transform (DFT) • Discrete Cosine Transform (DCT) • Matrices computations involves severe inner products comp.

  12. Conclusion • DA is a very efficient means to perform computations that are dominated by inner products • Coefficients can be time varying • Equations can be non-linear • Whenever the performance/cost ratio is critical, DA should be seriously considered as contender

More Related