Approximate multipliers attract a large interest in the scientific literature that proposes several circuits built with approximate 4-2 compressors. Due to the large number of proposed solutions, the designer who wishes to use an approximate 4-2 compressor is faced with the problem of selecting the right topology. In this paper, we present a comprehensive survey and comparison of approximate 4-2 compressors previously proposed in literature. We present also a novel approximate compressor, so that a total of twelve different approximate 4-2 compressors are analyzed. The investigated circuits are employed to design 8x 8 and 16x 16 multipliers, implemented in 28nm CMOS technology. For each operand size we analyze two multiplier configurations, with different levels of approximations, both signed and unsigned. Our study highlights that there is no unique winning approximate compressor topology since the best solution depends on the required precision, on the signedness of the multiplier and on the considered error metric.

Approximate multipliers are used in error-tolerant applications, sacrificing the accuracy of results to minimize power or delay. In this paper we investigate approximate multipliers using static segmentation. In these circuits a set of $m$ contiguous bits (a segment of $m$ bits) is extracted from each of the two $n$ -bits operand, the two segments are in input to a small $m\times m$ internal multiplier whose output is suitably shifted to obtain the result. We investigate both signed and unsigned multipliers, and for the latter we propose a new segmentation approach. We also present simple and effective correction techniques that can significantly reduce the approximation error with reduced hardware costs. We perform a detailed comparison with previously proposed approximate multipliers, considering a hardware implementation in 28 nm technology. The comparison shows that static segmented multipliers with the proposed correction technique have the desirable characteristic of being on (or close to) the Pareto-optimal frontier for both power vs normalized mean error distance and power vs mean relative error distance trade-off plots. These multipliers, therefore, are promising candidates for applications where their error performance is acceptable. This is confirmed by the results obtained for image processing and image classification applications.

This paper focuses on fixed-width multipliers with linear compensation function by investigating in detail the effect of coefficients quantization. New fixed-width multiplier topologies, with different accuracy versus hardware complexity trade-off, are obtained by varying the quantization scheme. Two topologies are in particular selected as the most effective ones. The first one is based on a uniform coefficient quantization, while the second topology uses a nonuniform quantization scheme. The novel fixed-width multiplier topologies exhibit better accuracy with respect to previous solutions, close to the theoretical lower bound.

Approximate computing is a technique that sacrifices the accuracy of the result for an advantage in terms of power, area, and speed. It is useful for error-tolerant applications such as image and video processing. Many approximate arithmetic circuits can be devised using approximate versions of the basic binary full adder that simply adds three bits to generate carry and sum. Therefore, the approximate full adder has been the subject of extensive investigation in recent years. In this paper we present a comprehensive analysis of approximate full adders, by synthesizing in a FinFET 14 nm technology the whole set of possible designs, achievable by modifying one or more entries of the full adder truth-table. Our exhaustive analysis shows that only a few out of the many thousands of synthesized circuits perform reasonably well as approximate full adders. Our analysis re-discovers the approximate full adders already proposed in the literature and identifies some new ones. Examples of using the newly discovered approximate full adders in typical error resilient applications are provided, showing the performance and the usefulness of approximate arithmetic circuit designs in the real-world.