Stochastic logic performs computation on data represented by random bit streams. The representation allows complex arithmetic to be performed with very simple logic, but it suffers from high latency and poor precision. Furthermore, the results are always somewhat inaccurate due to random fluctuations. The random or pseudorandom sources required to generate the representation are costly, consuming a majority of the circuit area (and diminishing the overall gains in area). In this paper, we show that randomness is not a requirement for this computational paradigm. If properly structured, the same arithmetical constructs can operate on  deterministic  bit streams, with the data represented uniformly by the fraction of 1's versus 0's. This paper presents three approaches for the computation: relatively prime stream lengths, rotation, and clock division. The three methods are evaluated on a collection of arithmetical functions. Unlike stochastic methods, all three of our deterministic methods produce completely accurate results. The cost of generating the deterministic streams is a small fraction of the cost of generating streams from random/pseudorandom sources. Most importantly, the latency is reduced by a factor of 1/2  n  , where  n  is the equivalent number of bits of precision.
['Devon Jenson', 'Marc D. Riedel']
A deterministic approach to stochastic computation