A parallel quantum histogram architecture

A parallel formulation of an algorithm for the histogram computation of n data items using an on-the-fly data decomposition and a novel quantum-like representation (QR) is developed. The QR transformation separates multiple data read operations from multiple bin update operations, thereby making it easier to bind data items into their corresponding histogram bins. Under this model, the steps required to compute the histogram is n/s + t steps, where s is a speedup factor, and t is associated with pipeline latency. Here, we show that an overall speedup factor s is available for up to an eightfold acceleration. Our evaluation also shows that each of these cells requires less area/time complexity compared to similar proposals found in the literature.