Design of Approximate Booth Squarer for Error-Tolerant Computing
To explore the benefits of approximate computing, this article proposes an approximate partial product generator for squarer (APPGS). Using APPGS, three designs of approximate radix-4 Booth squarers (ABS1, ABS2, and ABS3) are proposed. APPGS produces approximate partial products in r number of lea...
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Veröffentlicht in: | IEEE transactions on very large scale integration (VLSI) systems 2020-05, Vol.28 (5), p.1230-1241 |
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Sprache: | eng |
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Zusammenfassung: | To explore the benefits of approximate computing, this article proposes an approximate partial product generator for squarer (APPGS). Using APPGS, three designs of approximate radix-4 Booth squarers (ABS1, ABS2, and ABS3) are proposed. APPGS produces approximate partial products in r number of least significant columns of the partial product matrix. ABS2 and ABS3 utilize approximate adders and compressors with a novel input signal rearrangement method for the accumulation of approximate partial products. Moreover, the ABS3 features an error recovery module at k number of most significant columns of the approximate partial products. The proposed squarers with different values of r and k are simulated using 45-nm CMOS technology. The results indicate that the proposed squarers achieve optimized performance for both hardware and accuracy metrics. Compared to the exact Booth squarer, the 16-bit ABS1 with r=16 achieves a reduction of 13.6%, 22.2%, and 13.7% in power, delay, and area, respectively, with a normalized mean error distance (NMED) of 4.6\times 10^{-6} . The ABS2 has power, delay, and area savings of 25.8%, 33.8%, and 19.8%, respectively, with an NMED of 7.2\times 10^{-6} . The ABS3 with k=6 has 18.5% reduction in power, 29.4% reduction in delay, and 16.9% reduction in area with an NMED of 0.56\times 10^{-6} . The performance of the proposed squarers is evaluated with a telecommunication application, where the ABS3 with k=6 produces an output signal with a signal-to-noise ratio of 32.45 dB. |
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ISSN: | 1063-8210 1557-9999 |
DOI: | 10.1109/TVLSI.2020.2976131 |