DeBAM: Decoder-Based Approximate Multiplier for Low Power Applications

Nambi, Suresh; Kumar, Uppugunduru Anil; Radhakrishnan, Kavya; Venkatesan, Mythreye; Ahmed, Syed Ershad

doi:10.1109/les.2020.3045165

Cited by 12 publications

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“…Approximate computing consists in relaxing the constraint of an exact computation in order to trade the quality of the result with speed, area and power consumption [1,2]. As fundamental arithmetic blocks in signal processing, approximate multipliers have been widely explored in the last few years [3][4][5][6][7][8][9][10][11][12][13][14][15]. Several approximate techniques have been proposed, such as column truncation [5,6], approximate compressors [7,8], the use of error-tolerant adders [9], input truncations [10], vertical and horizontal cut [12] and input encoding [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…As fundamental arithmetic blocks in signal processing, approximate multipliers have been widely explored in the last few years [3][4][5][6][7][8][9][10][11][12][13][14][15]. Several approximate techniques have been proposed, such as column truncation [5,6], approximate compressors [7,8], the use of error-tolerant adders [9], input truncations [10], vertical and horizontal cut [12] and input encoding [13,14]. Generally, all these techniques exploit a simple error-correction technique, such as adding an error compensation constant to the approximate result in order to increase the accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

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Improving the Quality Degradation of Dynamically Configurable Approximate Multipliers via Data Correlation

Frustaci

2021

Electronics

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In the last few years, dynamically configurable approximate multipliers have been explored to tune the energy-quality trade-off in error-tolerant applications at runtime. Typically, the multiplier accuracy is adjusted by adding a constant correction factor equal to the multiplier mean error to the result, which is found offline assuming a predetermined input distribution. This paper describes a simple approach to update the correction term at runtime, thus adapting it to the actual incoming inputs. It takes advantage of the spatial and/or temporal correlation typically shown by input data in error-tolerant applications, such as image and video processing. When applied to a typical case study implemented with a commercial UTBB FDSOI 28 nm technology, the proposed approach shows an energy reduction of up to 34% at iso-quality and a quality improvement of up to +9 dB, −4× and +35% at iso-energy, in terms of peak-to-noise ratio (PSNR), normalized error distance (NED) and structural similarity index metric (SSIM) respectively, compared to the traditional technique based on a constant correction factor.

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