A pipeline architecture for computing cumulative hypergeometric distributions

The hypergeometric distribution is a widely used arithmetic function and is fundamental to many statistical sampling and statistical pattern recognition problems. Computation of the cumulative hypergeometric distribution function, H(a), is extremely time-consuming. As a result, many approximation algorithms have been proposed to evaluate the cumulative hypergeometric distribution. This paper describes a two-level pipeline architecture for computing H(a) with computation complexity reduced to c+a, where c is a constant. The main part of the design is a type of recurrence computation. A modular and systematic approach is suggested to implement the recurrence formula. The computation complexity of the proposed architecture is also compared with various other known methods. The highly regular structure of the design can lead to efficient VLSI implementation.