Exact and Approximate Squarers for Error-Tolerant Applications

Approximate computing is considered an innovative paradigm with wide applications to high performance and low power systems. These applications have relaxed requirements for accuracy, so they can tolerate errors in results and achieve high performance. In approximate computing, multipliers have been widely studied, but squarers (as similar schemes) have not received much attention. In this paper, an accurate squarer is designed based on a Radix-8 Booth-folding square algorithm to reduce the number of partial products and the depth of the partial product array. Several approximate squarers (R8AS1, R8AS2 and R8AS3) are proposed based on the exact squarer to reduce power and delay. Two approximate partial product generators are also designed to simplify the Radix-8 Booth square encoder in R8AS1 and R8AS2. In addition, approximate compressors with compensation are used in the partial product compression stage to reduce additional area and power consumption in R8AS3. Synthesis results for power, area, and delay at 28nm CMOS technology are presented. Compared with designs in the technical literature with the same accuracy, the proposed 16-bit designs reduce the PDP by 37%; in general, the PDP is decreased by up to 51%. Finally, the proposed approximate squarers are implemented in a square-law detector as a communication application and achieve an SNR close to 30dB. Also, the three proposed approximate squarers are applied to the k-means clustering algorithm for machine learning to accomplish high performance in classification.