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Approximate MAC Unit Using Static Segmentation

Gennaro Di Meo, Gerardo Saggese, A.G.M. Strollo, Davide De

2023IEEE Transactions on Emerging Topics in Computing15 citationsDOIOpen Access PDF

Abstract

In this paper we investigate a novel approximate multiply-and-accumulate (MAC) unit, that computes Y = A×B+C using static segmentation. The proposed architecture uses a unique carry-propagate adder and performs segmentation on the three operands A, B, and C, to reduce hardware cost. The circuit can be configured at design-time by two parameters. The first one controls the segmentation on A and B, while the second one controls the segmentation on C and the adder length. An error compensation technique is also employed, to reduce the approximation error. Error analysis and implementation results in 28nm CMOS for 8-bits multiplier with 20-bits and 24-bits addition are presented. The proposed approximate MACs outperform the state of the art, showing the largest power saving when the mean relative error distance (MRED) is larger than 2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> and 4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> for 20 and 24-bits addition, respectively. For MRED of about 6×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> the proposed approximate MAC with 20-bits addition exhibits a power reduction larger than 60% compared to the exact MAC and larger than 27% compared to the state-of-the-art approximate MACs. Application examples to image filtering and template matching show that proposed approximate circuits are good candidates in applications where their error performances are acceptable.

Topics & Concepts

AdderComputer scienceSegmentationAlgorithmOperandArithmeticMultiplier (economics)Computer hardwareArtificial intelligenceMathematicsMacroeconomicsEconomicsLatency (audio)TelecommunicationsLow-power high-performance VLSI designFerroelectric and Negative Capacitance DevicesAdvanced Memory and Neural Computing