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Parallel VLSI Architecture for Approximate Computation of Discrete Hadamard Transform


Using Big Data Analytics to Cr

3D Reconstruction in Canonical
Abstract


Conventionally, decimation-in-time (DIT) data-flowgraph (DFG) is used to obtain computing structures for discrete Hadamard transform (DHT). The fixed-width structures based on conventional approach, however, offers a marginal bit-saving and introduces relatively more truncation error. In this paper, we have derived decimation-in-frequency (DIF) DFG for DHT which is more convenient for approximation using truncation than the DIT-DFG. To achieve higher bit-saving with relatively less truncation error, we propose an efficient truncation model comprising of pre-truncation () and post-truncation ( ). Also, we present a logic optimized adder-subtractor design customized for DHT structure. Using the proposed truncation model, we have three variants of proposed structures for parallel computation of 2-D DHT. The proposed structure-2 for DHT size N = 8, wordsize w = 8, and approximation (1 = 2; 1 = 1; 2 = 2; 2 = 1) involves nearly 38% less area-delay-product (ADP) and 46% less energy per sample (EPS), and calculates outputs with almost the same accuracy as the existing fixed-width structure based on post-truncation. For the same word-size and N = 16, the proposed structure-2 for approximation (1 = 2; 1 = 2; 2 = 2; 2 = 2) involves nearly 36% less ADP and 44% less EPS than the existing structure and calculates outputs with the same accuracy. We have observed that reconstructed images obtained using the proposed structure-2 are less by 7dB and 2dB PSNR than those obtained using the existing fixed-width structures for N = 8 and 16, respectively.

KeyWords
Discrete Hadamard Transform, Approximate computation, VLSI



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