Multiplier (economics)

About: Multiplier (economics) is a research topic. Over the lifetime, 9462 publications have been published within this topic receiving 94556 citations. The topic is also known as: multiplier effect.

Two's Complement Pipeline Multipliers

Abstract: Digital filters and signal processors when realized in hardware often use serial transfer of data. Multipliers which are capable of accepting variable coefficients and data in sign and magnitude notation and producing serial products of the same length as the input data word have been known for some time. This concise paper addresses the design of multipliers capable of accepting data in 2's complement notation, or both data and coefficients in 2's complement notation. It also considers multiplier recoding techniques, such as the Booth algorithm. Specialized (fixed coefficient) multiplier designs are considered briefly. Finally, multiplier rounding and overflow characteristics are discussed, and a rough comparison is made between the complexity of the various designs.

Axiomatic Foundations of Multiplier Preferences

Abstract: This paper axiomatizes the robust control criterion of multiplier preferences introduced by Hansen and Sargent (2001). The axiomatization relates multiplier preferences to other classes of preferences studied in decision theory, in particular the variational preferences, recently introduced by Maccheroni, Marinacci and Rustichini (2006). The paper also establishes a link between the parameters of the multiplier criterion and the observable behavior of the agent. This link enables measurement of the parameters on the basis of observable choice data and provides a useful tool for applications.

The Social Multiplier

Abstract: In many cases, aggregate data is used to make inferences about individual level behavior If there are social interactions in which one person's actions influence his neighbor's incentives or information, then these inferences are inappropriate The presence of positive social interactions, or strategic complementarities, implies the existence of a social multiplier where aggregate relationships will overstate individual elasticities We present a brief model and then estimate the size of the social multiplier in three areas: the impact of education on wages, the impact of demographics on crime and group membership among Dartmouth roommates In all three areas there appears to be a significant social multiplier

Design of Power and Area Efficient Approximate Multipliers

Abstract: Approximate computing can decrease the design complexity with an increase in performance and power efficiency for error resilient applications. This brief deals with a new design approach for approximation of multipliers. The partial products of the multiplier are altered to introduce varying probability terms. Logic complexity of approximation is varied for the accumulation of altered partial products based on their probability. The proposed approximation is utilized in two variants of 16-bit multipliers. Synthesis results reveal that two proposed multipliers achieve power savings of 72% and 38%, respectively, compared to an exact multiplier. They have better precision when compared to existing approximate multipliers. Mean relative error figures are as low as 7.6% and 0.02% for the proposed approximate multipliers, which are better than the previous works. Performance of the proposed multipliers is evaluated with an image processing application, where one of the proposed models achieves the highest peak signal to noise ratio.

DRUM: A Dynamic Range Unbiased Multiplier for Approximate Applications

Abstract: Many applications for signal processing, computer vision and machine learning show an inherent tolerance to some computational error. This error resilience can be exploited to trade off accuracy for savings in power consumption and design area. Since multiplication is an essential arithmetic operation for these applications, in this paper we focus specifically on this operation and propose a novel approximate multiplier with a dynamic range selection scheme. We design the multiplier to have an unbiased error distribution, which leads to lower computational errors in real applications because errors cancel each other out, rather than accumulate, as the multiplier is used repeatedly for a computation. Our approximate multiplier design is also scalable, enabling designers to parameterize it depending on their accuracy and power targets. Furthermore, our multiplier benefits from a reduction in propagation delay, which enables its use on the critical path. We theoretically analyze the error of our design as a function of its parameters and evaluate its performance for a number of applications in image processing, and machine classification. We demonstrate that our design can achieve power savings of 54% -- 80%, while introducing bounded errors with a Gaussian distribution with near-zero average and standard deviations of 0.45% -- 3.61%. We also report power savings of up to 58% when using the proposed design in applications. We show that our design significantly outperforms other approximate multipliers recently proposed in the literature.