Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Complexity-Aware Quantization and Lightweight VLSI Implementation of FIR Filters | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 357906 14 pages doi 2011 357906 Research Article Complexity-Aware Quantization and Lightweight VLSI Implementation of FIR Filters Yu-Ting Kuo 1 Tay-Jyi Lin 2 and Chih-Wei Liu1 1 Department of Electronics Engineering National Chiao Tung University Hsinchu 300 Taiwan 2 Department of Computer Science and Information Engineering National Chung Cheng University Chiayi 621 Taiwan Correspondence should be addressed to Tay-Jyi Lin tjlin@ Received 1 June 2010 Revised 28 October 2010 Accepted 4 January 2011 Academic Editor David Novo Copyright 2011 Yu-Ting Kuo et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. The coefficient values and number representations of digital FIR filters have significant impacts on the complexity of their VLSI realizations and thus on the system cost and performance. So making a good tradeoff between implementation costs and quantization errors is essential for designing optimal FIR filters. This paper presents our complexity-aware quantization framework of FIR filters which allows the explicit tradeoffs between the hardware complexity and quantization error to facilitate FIR filter design exploration. A new common subexpression sharing method and systematic bit-serialization are also proposed for lightweight VLSI implementations. In our experiments the proposed framework saves 49 51 additions of the filters with 2 s complement coefficients and 10 20 of those with conventional signed-digit representations for comparable quantization errors. Moreover the bit-serialization can reduce 33 35 silicon area for less timing-critical applications. 1. Introduction Finite-impulse response FIR 1 filters are important building blocks of multimedia signal processing and .
