Stochastic Computing co-processing elements for Evolving Autonomous Data Partitioning

This paper proposes a hardware acceleration of a recently proposed evolving Autonomous Data Partitioning (ADP) algorithm by an unconventional computational technique as stochastic computing. The ADP algorithm is non-parametric and its evolving version creates data clouds from streaming data samples. Since the ADP algorithm calculates maximum and minimum Euclidean norms, distances and maximum/minimum arguments, proper hardware acceleration based on the Stochastic Computing design should reduce power consumption by degrading arithmetic precision in the future embedded system. The simulations of the 8-bit proposed design for different datasets show more or less impact on the clustering metrics due to arithmetic inaccuracies compared to the results obtained by the equivalent floating-point design. Despite these differences, in certain circumstances the Stochastic Computing design can outperform the floating-point design. In fact, the clustering quality metrics results obtained for different datasets are quite similar (or slightly inferior in some tests) to the results of evolving ADP original paper, in which all calculations were performed in floating-point precision.