Interfacing PDM MEMS Microphones with PFM Spiking Systems: Application for Neuromorphic Auditory Sensors
Authors: 
Daniel Gutierrez-Galan, Antonio Rios-Navarro, Juan Pedro Dominguez-Morales, Lourdes Duran-Lopez, Gabriel Jimenez-Moreno, Angel Jimenez-FernandezAuthors Info & Claims
Daniel Gutierrez-Galan, Antonio Rios-Navarro, Juan Pedro Dominguez-Morales, Lourdes Duran-Lopez, Gabriel Jimenez-Moreno, Angel Jimenez-FernandezAuthors Info & ClaimsPages 1281 - 1292
Abstract
Neuromorphic computation processes sensors output in the spiking domain, which presents constraints in many cases when converting information to spikes, loosing, as example, temporal accuracy. This paper presents a spike-based system to adapt audio information from low-power pulse-density modulation (PDM) microelectromechanical systems microphones into rate coded spike frequencies. These spikes could be directly used by the neuromorphic auditory sensor (NAS) for frequency decomposition in different bands, avoiding the analog or digital conversion to spike streams. This improves the time response of the NAS, allowing its use in more time restrictive applications. This adaptation was conducted in VHDL as an interface for PDM microphones, converting their pulses into temporal distributed spikes following a pulse-frequency modulation scheme with an accurate inter-spike-interval, known as PDM to spikes interface (PSI). We introduce a new architecture of spike-based band-pass filter to reject DC components and distribute spikes in time. This was tested in two scenarios, first as a stand-alone circuit for its characterization, and then integrated with a NAS for verification. The PSI achieves a total harmonic distortion of 46.18 dB and a signal-to-noise ratio of 63.47 dB, demands less than 1% of the resources of a Spartan-6 FPGA and its power consumption is around 7 mW.
References
[1]
Maluf N, Williams K (2004) An introduction to microelectromechanical systems engineering. Artech House
[2]
Smith LS Neuromorphic systems: Past, present and future Brain Inspired Cognitive Systems 2010 2008 167-182
[3]
Liu S, Rueckauer B, Ceolini E, Huber A, and Delbruck T Event-driven sensing for efficient perception: Vision and audition algorithms IEEE Signal Processing Magazine 2019 36 6 29-37
[4]
Bensimon M, Greenberg S, and Haiut M Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing Sensors 2021 21 4 1065
[5]
Sevuktekin NC, Varshney LR, Hanumolu PK, and Singer AC Signal processing foundations for time-based signal representations: Neurobiological parallels to engineered systems designed for energy efficiency or hardware simplicity IEEE Signal Processing Magazine 2019 36 6 38-50
[6]
Chan V, Liu S-C, and van Schaik A AER EAR: A matched silicon cochlea pair with address event representation interface IEEE Transactions on Circuits and Systems I: Regular Papers 2007 54 1 48-59
[7]
Hamilton TJ, Jin C, Van Schaik A, and Tapson J An active 2-D silicon cochlea IEEE Transactions on biomedical circuits and systems 2008 2 1 30-43
[8]
Wen B and Boahen K A silicon cochlea with active coupling IEEE transactions on biomedical circuits and systems 2009 3 6 444-455
[9]
Liu S.-C, Van Schaik A, Mincti B.A, Delbruck T (2010) Event-based 64-channel binaural silicon cochlea with q enhancement mechanisms. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 2027–2030 . IEEE
[10]
Kiselev I, Gao C, and Liu S-C Spiking Cochlea with System-level Local Automatic Gain Control 2022 IEEE Transactions on Circuits and Systems I Regular Papers
[11]
Mugliette C, Grech I, Casha O, Gatt E, Micallef J (2011) FPGA active digital cochlea model. In: 2011 18th IEEE International Conference on Electronics, Circuits, and Systems, pp. 699–702 . IEEE
[12]
Thakur C.S, Hamilton T.J, Tapson J, van Schaik A, Lyon R.F (2014) FPGA Implementation of the CAR Model of the Cochlea. In: 2014 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1853–1856 . IEEE
[13]
Lyon R (1982) A computational model of filtering, detection, and compression in the cochlea. In: ICASSP’82. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 7, pp. 1282–1285 . IEEE
[14]
Jiménez-Fernández A, Cerezuela-Escudero E, Miró-Amarante L, Domínguez-Morales MJ, de Asís Gómez-Rodríguez F, Linares-Barranco A, and Jiménez-Moreno G A binaural neuromorphic auditory sensor for FPGA: A spike signal processing approach IEEE transactions on neural networks and learning systems 2016 28 4 804-818
[15]
Jimenez-Fernandez A, Linares-Barranco A, Paz-Vicente R, Jiménez G, Civit A (2010) Building blocks for spikes signals processing. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 . IEEE
[16]
Schoepe T, Gutierrez-Galan D, Dominguez-Morales J.P, Jimenez-Fernandez A, Linares-Barranco A, Chicca E (2019) Neuromorphic sensory integration for combining sound source localization and collision avoidance. In: 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4 . IEEE
[17]
Dominguez-Morales JP, Jimenez-Fernandez AF, Dominguez-Morales MJ, and Jimenez-Moreno G Deep neural networks for the recognition and classification of heart murmurs using neuromorphic auditory sensors IEEE transactions on biomedical circuits and systems 2017 12 1 24-34
[18]
Dominguez-Morales J.P, Liu Q, James R, Gutierrez-Galan D, Jimenez-Fernandez A, Davidson S, Furber S (2018) Deep spiking neural network model for time-variant signals classification: a real-time speech recognition approach. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 . IEEE
[19]
Rasetto M, Dominguez-Morales J.P, Jimenez-Fernandez A, Benosman R (2021) Event Based Time-Vectors for auditory features extraction: a neuromorphic approach for low power audio recognition. arXiv preprint arXiv:2112.07011
[20]
Indiveri G and Sandamirskaya Y The importance of space and time for signal processing in neuromorphic agents: The challenge of developing low-power, autonomous agents that interact with the environment IEEE Signal Processing Magazine 2019 36 6 16-28
[21]
Iakymchuk T, Rosado A, Serrano-Gotarredona T, Linares-Barranco B, Jiménez-Fernandez A, Linares-Barranco A, Jiménez-Moreno G (2014) An AER handshake-less modular infrastructure PCB with x8 2.5 Gbps LVDS serial links. In: 2014 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1556–1559 . IEEE
[22]
da Silva B, Segers L, Braeken A, Steenhaut K, Touhafi A (2018) A low-power FPGA-based architecture for microphone arrays in wireless sensor networks. In: Voros, N., Huebner, M., Keramidas, G., Goehringer, D., Antonopoulos, C., Diniz, P.C. (eds.) Applied Reconfigurable Computing. Architectures, Tools, and Applications, pp. 281–293. Springer, Cham
[23]
Berner R, Delbruck T, Civit-Balcells A, Linares-Barranco A (2007) A 5 Meps \$100 USB 2.0 address-event monitor-sequencer interface. In: 2007 IEEE International Symposium on Circuits and Systems, pp. 2451–2454 . IEEE
[24]
Delbruck T (2008) Frame-free dynamic digital vision. In: Proceedings of Intl. Symp. on Secure-Life Electronics, Advanced Electronics for Quality Life and Society, pp. 21–26 . Tokyo
[25]
Dominguez-Morales JP, Jimenez-Fernandez A, Dominguez-Morales M, and Jimenez-Moreno G NAVIS: Neuromorphic Auditory VISualizer tool Neurocomputing 2017 237 418-422
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Kluwer Academic Publishers
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Published: 25 July 2022
Accepted: 16 June 2022
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