Generating unbiased permutations at run time has traditionally been accomplished through application specific optimized combinational logic and has been limited to very small permutations. For generating unbiased permutations of any larger size, variations of the memory dependent Fisher-Yates algorithm are known to be an optimal solution in software and have been relied on as a hardware solution even to this day. However, in hardware, this thesis proves Fisher-Yates to be a suboptimal solution. This thesis will show variations of Fisher-Yates to be suboptimal by proposing an alternate method that does not rely on memory and outperforms Fisher-Yates based permutation generators, while still able to scale to very large sized permutations. This thesis also proves that this proposed method is unbiased and requires a minimal input. Lastly, this thesis demonstrates a means to scale the proposed method to any sized permutations and also to produce optimal partial permutations. / Master of Science / In computing, some applications need the ability to shuffle or rearrange items based on run time information during their normal operations. A similar task is a partial shuffle where only an information dependent selection of the total items is returned in a shuffled order. Initially, there may be the assumption that these are trivial tasks. However, the applications that rely on this ability are typically related to security which requires repeatable, unbiased operations. These requirements quickly turn seemingly simple tasks to complex. Worse, often they are done incorrectly and only appear to meet these requirements, which has disastrous implications for security. A current and dominating method to shuffle items that meets these requirements was developed over fifty years ago and is based on an even older algorithm refer to as Fisher-Yates, after its original authors. Fisher-Yates based methods shuffle items in memory, which is seen as advantageous in software but only serves as a disadvantage in hardware since memory access is significantly slower than other operations. Additionally, when performing a partial shuffle, Fisher-Yates methods require the same resources as when performing a complete shuffle. This is due to the fact that, with Fisher-Yates methods, each element in a shuffle is dependent on all of the other elements. Alternate methods to meet these requirements are known but are only able to shuffle a very small number of items before they become too slow for practical use. To combat the disadvantages current methods of shuffling possess, this thesis proposes an alternate approach to performing shuffles. This alternate approach meets the previously stated requirements while outperforming current methods. This alternate approach is also able to be extended to shuffling any number of items while maintaining a useable level of performance. Further, unlike current popular shuffling methods, the proposed method has no inter-item dependency and thus offers great advantages over current popular methods with partial shuffles.