An Optimization Technique for PMF Estimation in Approximate Circuits

Dou, Yuqin; Wang, Chenghua

doi:10.1007/s11390-023-2544-z

Cited by 3 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various techniques have been proposed in the literature for the error estimation of approximate adders [19][20][21][22][23][24][25][26][27]. In [19], Liu et al proposed a framework for the error analysis of approximate adders used specifically for the accumulation of partial products in an approximate multiplier.…”

Section: State-of-the-art and Their Key Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

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

Hanif,

Hafiz,

Shafique

2023

Information

View full text Add to dashboard Cite

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.

show abstract

Section: State-of-the-art and Their Key Limitationsmentioning

confidence: 99%

“…Apart from the abovementioned works, recently, Dou et al [24] presented a PMFbased error estimation technique. However, they assumed the PMFs of the error to be independent of the inputs, and they also assumed the nodes to be independent of one another.…”

Section: State-of-the-art and Their Key Limitationsmentioning

confidence: 99%