Efficient signal processing algorithms are important for embedded and power-limited applications since, by reducing the number of computations, power consumption can be reduced significantly. Similarly, efficient algorithms are also critical to very large scale applications such as video processing and four-dimensional medical imaging. This self-contained guide, the only one of its kind, enables engineers to find the optimum fast algorithm for a specific application. It presents a broad range of computationally-efficient algorithms, describes their structure and implementation, and compares their relative strengths for given problems. All the necessary background mathematics is included and theorems are rigorously proved, so all the information needed to learn and apply the techniques is provided in one convenient guide. With this practical reference, researchers and practitioners in electrical engineering, applied mathematics, and computer science can reduce power dissipation for low-end applications of signal processing, and extend the reach of high-end applications.

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Fast Algorithms for Signal Processing

The DCT and the DWT are used in a number of emerging DSP applications, such as, HD video compression, biomedical imaging, and smart antenna beamformers for wireless communications and radar. Of late, there has been much interest on fast algorithms for the computation of the above transforms using multiplier-free approximations because they result in low power and low complexity systems. Approximate methods rely on the trade-off of accuracy for lower power and/or circuit complexity/chip-area. This paper provides a detailed review of VLSI architectures and CAS implementations for both DCT/DWTs, which can be designed either for higher-accuracy or for low-power consumption. This article covers both recent theoretical advancements on discrete transforms in addition to an overview of existing VLSI architectures. The paper also discusses error free VLSI architectures that provides high accuracy systems and approximate architectures that offer high computational gain making them highly attractive for real-world applications that are subject to constraints in both chip-area as well as power. The methods discussed in the paper can be used in the design of emerging low-power digital systems having lowest complexity at the cost of a loss in accuracy?the optimal trade-off of computational accuracy for lowest possible complexity and power. A complete synopsis of available techniques, algorithms and FPGA/VLSI realizations are discussed in the paper.

Low-Power VLSI Architectures for DCT\/DWT: Precision vs Approximation for HD Video, Biomedical, and Smart Antenna Applications

This paper surveys techniques for multiplying elements of various commutative rings. It covers Karatsuba multiplication, dual Karatsuba multiplication, Toom multiplication, dual Toom multiplication, the FFT trick, the twisted FFT trick, the split-radix FFT trick, Good’s trick, the SchönhageStrassen trick, Schönhage’s trick, Nussbaumer’s trick, the cyclic SchönhageStrassen trick, and the Cantor-Kaltofen theorem. It emphasizes the underlying ring homomorphisms.

Multidigit multiplication for mathematicians

In this paper we introduce an algebraic integer encoding scheme for the basis matrix elements of 8/spl times/8 DCTs and IDCTs. In particular, we encode the function cos(/spl pi//16) and generate the other matrix elements using standard trigonometric identities. This encoding technique eliminates the requirement to approximate the matrix elements; rather we use algebraic 'placeholders' for them. Using this encoding scheme we are able to produce a multiplication free implementation of the Feig-Winograd algorithm.

A new DCT algorithm based on encoding algebraic integers

An algebraic integer (AI)-based time-multiplexed row-parallel architecture and two final reconstruction step (FRS) algorithms are proposed for the implementation of bivariate AI encoded 2-D discrete cosine transform (DCT). The architecture directly realizes an error-free 2-D DCT without using FRSs between row-column transforms, leading to an 8 × 8 2-D DCT that is entirely free of quantization errors in AI basis. As a result, the user-selectable accuracy for each of the coefficients in the FRS facilitates each of the 64 coefficients to have its precision set independently of others, avoiding the leakage of quantization noise between channels as is the case for published DCT designs. The proposed FRS uses two approaches based on: 1) optimized Dempster-Macleod multipliers, and 2) expansion factor scaling. This architecture enables low-noise high-dynamic range applications in digital video processing that requires full control of the finite-precision computation of the 2-D DCT. The proposed architectures and FRS techniques are experimentally verified and validated using hardware implementations that are physically realized and verified on field-programmable gate array (FPGA) chip. Six designs, for 4-bit and 8-bit input word sizes, using the two proposed FRS schemes, have been designed, simulated, physically implemented, and measured. The maximum clock rate and block rate achieved among 8-bit input designs are 307.787 MHz and 38.47 MHz, respectively, implying a pixel rate of 8 × 307.787≈2.462 GHz if eventually embedded in a real- time video-processing system. The equivalent frame rate is about 1187.35Hz for the image size of 1920 × 1080. All implementations are functional on a Xilinx Virtex-6 XC6VLX240T FPGA device.

A Row-Parallel 8 $\,\times\,$ 8 2-D DCT Architecture Using Algebraic Integer-Based Exact Computation

A new method is described for computing an N = R^{m} = 2^{\upsilon m} -point complex discrete Fourier transform that uses quantization within a dense ring of algebraic integers in conjunction with a residue number system over this ring The algebraic and analytic foundations for the technique are derived and discussed The architecture for a radix- R fast Fourier transform algorithm using a residue number system over Z[\omega] , where \omega is a primitive R th root of unity, is developed; and range and error estimates for this algorithm are derived

J. Cozzens

Papers

Computing the discrete Fourier transform using residue number systems in a ring of algebraic integers