In this work, we introduce, for the first time, the design of a compact neuromorphic architecture to efficiently support a filtered-x error-coded affine projection-like (FXECAP-L) algorithm that is based on affine projection (AP) algorithms for active noise cancellation (ANC) in an acoustic duct. To date, few practical ANC implementations have used AP algorithms because of their high computational complexity, despite providing fast convergence speeds. One of the main factors that increases their computational complexity is linked to the dimensions of the matrix used in the AP algorithm’s computations. Evidently, the largest dimensions of the matrix increase the convergence speed of the AP algorithms by paying a penalty in terms of area consumption. However, convergence speed is crucial in ANC applications since this factor determines the speed at which the noise is canceled. Recently, an FXECAP-L algorithm with evolving order has been proposed to dynamically reduce the dimensions of the matrix by maintaining the convergence speed of AP algorithms. Here, we propose a compact neuromorphic architecture with a dynamic routing mechanism to efficiently implement the evolutionary method of the FXECAP-L algorithm by creating a virtual matrix, whose dimensions can be modified over the filter processing. In this way, we avoid spending a large amount of memory to save the largest matrix elements. In addition, the inclusion of the dynamic routing mechanism in the proposed neuromorphic architecture has allowed us to guarantee low area consumption since the neuromorphic architecture is capable of simulating different adaptive structures without modifying its structure. Here, the neuromorphic architecture has been configured as the system identification and ANC controller for practical noise cancellation in an acoustic duct. Our results have demonstrated that the combination of the properties of the FXECAP-L algorithm and the implementation techniques generate a versatile signal processing development tool that can be used in practical real-time ANC applications.

在这项工作中，我们首次介绍了紧凑的神经形态架构的设计，以有效地支持基于仿射投影（AP）算法的滤波x错误编码仿射投影样（FXECAP-L）算法。声导管中的主动降噪（ANC）。迄今为止，尽管提供了快速的收敛速度，但由于它们的高计算复杂性，很少有实际的ANC实现使用AP算法。增加其计算复杂度的主要因素之一与AP算法的计算中使用的矩阵的尺寸有关。显然，矩阵的最大尺寸通过付出面积消耗的代价来提高AP算法的收敛速度。然而，收敛速度在ANC应用中至关重要，因为该因素决定了消除噪声的速度。最近，提出了一种具有进化级的FXECAP-L算法，以通过保持AP算法的收敛速度来动态减小矩阵的维数。在这里，我们提出了一种具有动态路由机制的紧凑型神经形态架构，通过创建虚拟矩阵来有效实现FXECAP-L算法的进化方法，该矩阵可以在过滤处理中进行修改。这样，我们避免花费大量内存来保存最大的矩阵元素。此外，由于神经形态架构能够模拟不同的自适应结构而不修改其结构，因此在拟议的神经形态架构中包含动态路由机制使我们能够保证较低的面积消耗。在这里，神经形态架构已被配置为系统识别和ANC控制器，用于在声导管中实际消除噪声。我们的结果表明，FXECAP-L算法的属性和实现技术的结合产生了一种通用的信号处理开发工具，可以在实际的实时ANC应用中使用。神经形态架构已被配置为系统识别和ANC控制器，用于消音管道中的实际噪声。我们的结果表明，FXECAP-L算法的属性和实现技术的结合产生了一种通用的信号处理开发工具，可以在实际的实时ANC应用中使用。神经形态架构已被配置为系统识别和ANC控制器，用于消音管道中的实际噪声。我们的结果表明，FXECAP-L算法的属性和实现技术的结合产生了一种通用的信号处理开发工具，可以在实际的实时ANC应用中使用。