Synthesis of Approximate Parallel-Prefix Adders

Stefanidis, Apostolos; Zoumpoulidou, Ioanna; Filippas, Dionysios; Dimitrakopoulos, Giorgos; Sirakoulis, Georgios Ch.

doi:10.1109/tvlsi.2023.3287631

Cited by 2 publications

(1 citation statement)

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“…In the past few years, the design of low-latency approximate adders has received a significant amount of attention from the approximate computing community [17,[29][30][31][32][33][34], to improve the performance, area and power efficiency of error-resilient applications. A dominant subset of these adders, which includes segmented and carry select adders (Figure 1 and Figure 2, respectively), is based on the idea that, for most of the input combinations, the longest carry propagation chain is less than the complete length (N) of the adder [28].…”

Section: Introductionmentioning

confidence: 99%

DAEM: A Data- and Application-Aware Error Analysis Methodology for Approximate Adders

Hanif,

Hafiz,

Shafique

2023

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Approximate adders are some of the fundamental arithmetic operators that are being employed in error-resilient applications, to achieve performance/energy/area gains. This improvement usually comes at the cost of some accuracy and, therefore, requires prior error analysis, to select an approximate adder variant that provides acceptable accuracy. Most of the state-of-the-art error analysis techniques for approximate adders assume input bits and operands to be independent of one another, while some also assume the operands to be uniformly distributed. In this paper, we analyze the impact of these assumptions on the accuracy of error estimation techniques, and we highlight the need to address these assumptions, to achieve better and more realistic quality estimates. Based on our analysis, we propose DAEM, a data- and application-aware error analysis methodology for approximate adders. Unlike existing error analysis models, we neither assume the adder operands to be uniformly distributed nor assume them to be independent. Specifically, we use 2D joint input probability mass functions (PMFs), populated using sample data, in order to incorporate the data and application knowledge in the analysis. These 2D joint input PMFs, along with 2D error maps of approximate adders, are used to estimate the error PMF of an adder network. The error PMF is then utilized to compute different error measures, such as the mean squared error (MSE) and mean error distance (MED). We evaluate the proposed error analysis methodology on audio and video processing applications, and we demonstrate that our methodology provides error estimates having a better correlation with the simulation results, as compared to the state-of-the-art techniques.

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