FDSOI-Based Analog Computing for Ultra-Efficient Hamming Distance Similarity Calculation

Abstract

Computing the similarity between two binary strings is a frequently used operation in cryptography, machine learning, and other areas. The Hamming distance is a simple yet costly to compute similarity metric. A common way is to XOR both binary input strings and then count the number of 1s. Especially the latter popcount part is inefficient with purely digital circuits. In this paper, a novel analog circuit is proposed to compute the Hamming distance in an ultra-efficient way. Contrary to the major trend in the state of the art, no emerging technology is required. Instead, the unique feature of the mature FDSOI transistor technology is exploited for the first time to perform analog-based similarity calculation. Thanks to the additional back gate available in this technology, the transistor’s threshold voltage can be modulated by more than 1. Through this key feature, an ultra-efficient analog computing is realized, replacing the inefficient digital popcount traditionally built from expensive adder tree structures. The design is evaluated with an FDSOI transistor model calibrated with industrial measurements. The energy-delay product is at least 24 smaller than purely digital implementations and the transistor count is reduced by over 2.6.