1 / 7

EFFECT OF DATA CORRELATION ON FIR FILTER

EFFECT OF DATA CORRELATION ON FIR FILTER. Deepaknath Tandur K Sumit Ahuja. OBJECTIVE . Implement an FIR filter as RTL netlist using VHDL in two forms: No shared resources Shared resources Select 5 input data streams characterized by different correlations.

bell
Download Presentation

EFFECT OF DATA CORRELATION ON FIR FILTER

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. EFFECT OF DATA CORRELATION ON FIR FILTER Deepaknath Tandur K Sumit Ahuja

  2. OBJECTIVE • Implement an FIR filter as RTL netlist using VHDL in two forms: • No shared resources • Shared resources • Select 5 input data streams characterized by different correlations. • Synthesize the two implementations down to the gate-level. • Estimate power consumption and discuss the relation between power and data correlation.

  3. u[k] u[k-1] u[k-2] u[k-3] u[k-4] u[k-5] h0 b0 h1 h2 h3 h4 h5 * * * * * * y[k] + + + + + Fig1: Direct Realization of FIR filter with unshared resources. UNSHARED RESOURCE IMPLEMENTATION

  4. * * u[k-2] u[k-5] u[k] h0 u[k-1] u[k-3] h3 u[k-4] h4 h1 h2 h5 Time 1 + * Time 2 Time 3 + * Time 4 + Time 5 * Time 6 + Time 7 + y[k] * Fig2: Direct Realization of FIR filter with shared resources. SHARED RESOURCE IMPLEMENTATION

  5. DATA CORRELATION & INPUT PATTERNS • Highly Correlated Input pattern 00000000 00000001 00000011 and so on. • Correlated Data Pattern 00000000 00000001 00000010 and so on. • Mid-correlated Data Pattern 00000000 00000011 00001111 and so on. • Uncorrelated Data Pattern 00000000 00001111 11111111 and so on. • Highly Uncorrelated Data Pattern 00000000 11111111 00000000 and so on.

  6. RESULTS

  7. COMPARISON & CONCLUSIONS: • Data correlation more observable on parallel implementation. • In resource sharing, the data correlation is destroyed. • Parallel implementation is faster as compared to shared implementation. • Dynamic power consumption is highly variable in case of parallel implementation. • Thus power dissipation due to variance in resource sharing to be considered while designing.

More Related