Acceleration of Fast Sample Entropy Towards Biomedical Applications on FPGAs

Sample Entropy (SampEn) is an information en-tropy algorithm widely used for complexity analysis and chaos estimation in many applications. In particular, SampEn measures complexity of time series by the conditional probability of the inner pattern. Unfortunately, the straightforward implementation of SampEn is quadratic time complexity, restricting its real-time analysis ability for health applications and long-term data analysis. Although researchers have proposed fast versions of SampEn to avoid unnecessary comparisons, they have not been accelerated yet due to their performance bottleneck in the complex similarity pair process. In this paper, we evaluate fast SampEn algorithms by employing multi-source biomedical signals on an Field-Programmable Gate Arrays (FPGA). Since fast SampEn algorithms based of a pre-sorting stage promise to outperform other SampEn algorithms, Lightweight SampEn based on Merge Sort is here implemented and optimized. Dif-ferent type of optimizations, that can be generalized for similar Lightweight-based SampEn algorithms, are used to reduce the overall latency while the data throughput is increased. A load balancing strategy for multi similarity pair modules is also proposed to solve the unbalancing loads, a bottleneck when increasing the execution parallelism of this type of algorithms. As a result, the proposed SampEn architecture runs 10 times faster than the fastest SampEn implementation on a modern CPU.