TY - JOUR
T1 - A compact neuromorphic architecture with dynamic multiplexing to efficiently compute a nearest Kronecker product decomposition based RLS-NLMS algorithm for active noise control headphones
AU - Vazquez, Angel
AU - Garcia, Luis
AU - Toscano, Karina
AU - Sanchez, Juan Carlos
AU - Duchen, Gonzalo
AU - Perez, Hector
AU - Avalos, Juan Gerardo
AU - Sanchez, Giovanny
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/9/7
Y1 - 2022/9/7
N2 - Recently, embedded applications in resource-constrained electronic appliances are increasingly being used for noise reduction. Specifically, modern headphones are used to mitigate the environmental noise effects using advanced active noise control (ANC) systems. Despite achieving great performance, there is still a great challenge to develop compact ANC headphones since these devices contain a limited area. In addition, there are more challenges linked to improvement of the convergence rate, tracking, and complexity of the adaptive algorithm which is used in these devices to efficiently reduce the environmental noise. Specifically, the convergence properties can be improved by using the cutting-edge advanced adaptive algorithms along with the nearest Kronecker product (NKP) decomposition. In this work, we present a new neuromorphic architecture to efficiently compute an improved variant of the Kronecker product recursive least-squares (RLS). To achieve this architecture, we present three contributions; (1) we made a combination between the filtered-X RLS algorithm and normalized least mean squares (NLMS) algorithm to decrease the computational complexity by reducing the number of arithmetic operations required to efficiently compute the filter coefficients. Therefore, the proposed method requires fewer operations when compared with conventional RLS and RLS-NKP algorithms, respectively; (2) we use the spiking neural P (SN P) systems along with their advanced variants, such as rules on the synapses, colored spikes and dendritic delays to design two compact parallel arithmetic circuits (adder and divisor). In this way, the proposed method can be computed at high processing speeds and expending low area; (3) we propose a new digital neuromorphic architecture to be applied in active noise cancellation in headphones. Specifically, we propose a new adaptive unit core, which contains the proposed parallel neural arithmetic circuits, to perform dual filter operations, i.e, the proposed unit is capable of simulating two adaptive algorithms by using the same core. To achieve this we use the dynamic multiplexing technique. Therefore, our proposal exhibits low area consumption. As a consequence, the implementation of the proposed method can be easily integrated into resource-constrained ANC headphones.
AB - Recently, embedded applications in resource-constrained electronic appliances are increasingly being used for noise reduction. Specifically, modern headphones are used to mitigate the environmental noise effects using advanced active noise control (ANC) systems. Despite achieving great performance, there is still a great challenge to develop compact ANC headphones since these devices contain a limited area. In addition, there are more challenges linked to improvement of the convergence rate, tracking, and complexity of the adaptive algorithm which is used in these devices to efficiently reduce the environmental noise. Specifically, the convergence properties can be improved by using the cutting-edge advanced adaptive algorithms along with the nearest Kronecker product (NKP) decomposition. In this work, we present a new neuromorphic architecture to efficiently compute an improved variant of the Kronecker product recursive least-squares (RLS). To achieve this architecture, we present three contributions; (1) we made a combination between the filtered-X RLS algorithm and normalized least mean squares (NLMS) algorithm to decrease the computational complexity by reducing the number of arithmetic operations required to efficiently compute the filter coefficients. Therefore, the proposed method requires fewer operations when compared with conventional RLS and RLS-NKP algorithms, respectively; (2) we use the spiking neural P (SN P) systems along with their advanced variants, such as rules on the synapses, colored spikes and dendritic delays to design two compact parallel arithmetic circuits (adder and divisor). In this way, the proposed method can be computed at high processing speeds and expending low area; (3) we propose a new digital neuromorphic architecture to be applied in active noise cancellation in headphones. Specifically, we propose a new adaptive unit core, which contains the proposed parallel neural arithmetic circuits, to perform dual filter operations, i.e, the proposed unit is capable of simulating two adaptive algorithms by using the same core. To achieve this we use the dynamic multiplexing technique. Therefore, our proposal exhibits low area consumption. As a consequence, the implementation of the proposed method can be easily integrated into resource-constrained ANC headphones.
KW - Active noise control headphones
KW - FPGA
KW - Nearest Kronecker product
KW - Neuromorphic architecture
KW - Recursive least-squares algorithm
UR - http://www.scopus.com/inward/record.url?scp=85133548867&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2022.06.109
DO - 10.1016/j.neucom.2022.06.109
M3 - Artículo
AN - SCOPUS:85133548867
SN - 0925-2312
VL - 503
SP - 1
EP - 16
JO - Neurocomputing
JF - Neurocomputing
ER -