during the last decade, there has been growing interest in developing active noise cancellation (ANC) sys-tems since they have emerged as a potential solution in the noise reduction of indoor or outdoor sources. Regar...
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during the last decade, there has been growing interest in developing active noise cancellation (ANC) sys-tems since they have emerged as a potential solution in the noise reduction of indoor or outdoor sources. Regarding the last point, if the users need to reduce the noise in some extended areas, a very-large amount of microphones andloudspeakers are required. As a consequence, the ANC system demands a large amount of computation. One potential solution can be given if the information is partitioned anddistributed into several computing systems since the use of a centralized computing system could be insufficient. However, current distributed strategies demand a huge computationally cost. Therefore, the development of an efficient distributed ANC system to be applied in practical real-time ANC applica-tions is a challenging task. Here, we present two contributions, which involve the development of a new variant of the filtered-x set membership affine projection-like algorithm to save a large amount of com-putational cost and the design of a FPGA-baseddistributed neural processor to efficiently simulate the proposedalgorithm. Specifically, we improve two aspects to create a compact and high-performance dis-tributed neural processor. The first aspect is linked to the improvement of the processing system. In par-ticular, we make extraordinary efforts to optimize existing spiking neural arithmetic circuits, which are highly demanded in the computation of the proposedalgorithm. The second improvement is related to the development of a new communication scheme based on cutting-edge variants of spiking neural P (SN P) systems to efficiently perform the data distribution between multiple FPGAs. To demonstrate the computational capabilities of the proposed FPGA-baseddistributed neural processor, we develop an acoustic sensor network as proof-of-concept. Our results have demonstrated that the proposeddis-tributed FPGA-based neural processor can be used in practical r
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