Approximate computing is an emerging trend in digital design that trades off the requirement of exact computation for improved speed and power performance. This paper proposes novel approximate compressors and an algorithm to exploit them for the design of efficient approximate multipliers. By using the proposed approach, we have synthesized approximate multipliers for several operand lengths using a 40-nm library. Comparison with previously presented approximated multipliers shows that the proposed circuits provide better power or speed for a target precision. Applications to image filtering and to adaptive least mean squares filtering are also presented in the paper.
Piecewise polynomial interpolation is a well-established technique for hardware function evaluation. The paper describes a novel technique to minimize polynomial coefficients wordlength with the aim of obtaining either exact or faithful rounding at a reduced hardware cost. The standard approaches employed in literature subdivide the design of piecewise-polynomial interpolators into three steps (coefficients calculation, coefficients quantization and arithmetic hardware optimization) and estimate conservatively the overall approximation error as the sum of the error components arising in each step. The proposed technique, using Integer Linear Programming (ILP), optimizes the polynomial coefficients taking into account all error components simultaneously. This gives two advantages. Firstly, we can obtain exactly rounded approximations; secondly, for faithfully rounded interpolators, we avoid any overdesign due to pessimistic assumptions on error components, optimizing in this way the resulting hardware. The proposed ILP based algorithm requires an acceptable CPU time (from few seconds to tens of minutes) and is suited for approximations up to, maximum, 24 input bits. The results compare favorably with previously published data. We present synthesis results in 28 nm and 90 nm CMOS technologies, to further assess the effectiveness of the proposed approach
A variable latency adder (VLA) reduces average addition time by using speculation: the exact arithmetic function is replaced by an approximated one, that is faster and gives correct results most of the times. When speculation fails, an error detection and correction circuit gives the correct result in the following clock cycle. Previous papers investigate VLAs based on Kogge-Stone, Han-Carlson or carry select topologies, speculating that carry propagation involves only a few consecutive bits. In several applications using 2's complement representation, however, operands have a Gaussian distribution and a nontrivial portion of carry chains can be as long as the adder size. In this paper we propose five novel VLA architectures, based on Brent-Kung, Ladner-Fisher, Sklansky, Hybrid Han-Carlson, and Carry increment parallel-prefix topologies. Moreover, we present a new efficient error detection and correction technique, that makes proposed VLAs suitable for applications using 2's complement representation. In order to investigate VLAs performances, proposed architectures have been synthesized using the UMC 65 nm library, for operand lengths ranging from 32 to 128 bits. Obtained results show that proposed VLAs outperform previous speculative architectures and standard (non-speculative) adders when high-speed is required
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