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Energy-Delay Efficient Segmented Approximate Adder With Smart Chaining
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AI
DOI：10.1109/TC.2024.3500371.png)
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Abstract
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Approximate computing is a promising approach for high-performance, and low-energy computation in inherently error-tolerant applications. This paper proposes an approximate adder comprising a constant-truncation block in the least significant part and several non-overlapping summation blocks in the more significant parts of the adder. The carry-in of each block is supplied using the most significant bit of one of the input operands from the earlier block. In the most significant block, two more-precise approaches are used to generate candidate values for the carry-in. The final value of the carry-in for this block is selected based on the values of the input operands. In fact, the proposed approximate adder is input-aware, and dynamically adjusts its operation in one or two cycles to improve accuracy while limiting the average delay. The experimental results indicate that the proposed adder has a better quality-effort tradeoff than state-of-the-art approximate adders. Different configurations of the proposed adder improve delay, energy, and the energy-delay product (EDP) by 78%, 72%, and 87%, respectively, when compared to state-of-the-art approximate adders, all without any loss in accuracy. Additionally, the efficiency of the proposed adder is confirmed in both image dithering and stock price prediction through regression.
Keywords:
Approximate adder
low-energy computation
approximate image processing
image dithering
stock price prediction
regression
Approximate adder
low-energy computation
approximate image processing
image dithering
stock price prediction
regression
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3.8
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5.4K
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