access icon openaccess Resource optimised reconfigurable modular parallel pipelined stochastic approximation-based self-tuning regulator architecture with reduced latency

Present self-tuning regulator architectures based on recursive least-square estimation are computationally expensive and require large amount of resources and time in generating the first control signal due to computational bottlenecks imposed by the calculations involved in estimation stage, different stages of matrix multiplications and the number of intermediate variables at each iteration and precludes its use in applications that have fast required response times and those which run on embedded computing platforms with low-power or low-cost requirements with constraints on resource usage. A salient feature of this study is that a new modular parallel pipelined stochastic approximation-based self-tuning regulator architecture which reduces the time required to generate the first control signal, reduces resource usage and reduces the number of intermediate variables is proposed. Fast matrix multiplication, pipelining and high-speed arithmetic function implementations were used for improving the performance. Results of implementation demonstrate that the proposed architecture has an improvement in control signal generation time by 38% and reduction in resource usage by 41% in terms of multipliers and 44.4% in terms of adders compared with the best existing related work, opening up new possibilities for the application of online embedded self-tuning regulators.

Inspec keywords: matrix algebra; pipeline processing; approximation theory; stochastic processes; least squares approximations; parallel architectures

Other keywords: salient feature; parallel pipelined stochastic approximation; embedded computing platforms; matrix multiplication; arithmetic function implementations; self tuning regulator architecture; self-tuning regulator architecture; resource optimised reconfigurable modular parallel pipelined stochastic approximation; computational bottlenecks; matrix multiplications; recursive least-square estimation

Subjects: Other topics in statistics; Parallel architecture; Interpolation and function approximation (numerical analysis); Linear algebra (numerical analysis); Multiprocessing systems

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