Absolute Subtraction and Division Circuits Using Uncorrelated Random Bitstreams in Stochastic Computing

Different from conventional deterministic binary computing, stochastic computing (SC) utilizes random binary bitstreams to implement arithmetic functions. It has shown advantages in hardware cost and fault tolerance in applications such as image processing. In contrast stretching and edge detection, specifically, division and absolute subtraction are important functions. However, it is challenging to directly compute these functions in SC, especially when uncorrelated bitstreams are used. In this paper, a counter-based unipolar scaled absolute subtractor (UCASub) is first proposed for using two uncorrelated bitstreams. Based on the UCASub, a bipolar scaled absolute subtractor and unipolar and bipolar dividers are further proposed for using uncorrelated bitstreams. Experimental results show that these circuits are more accurate with lower mean squared errors and similar hardware overhead when compared with previous designs.