TinyML acoustic classification using RAMAN accelerator and neuromorphic cochlea

The growing use of acoustic classification systems in edge computing and Internet of Things (IoT) devices has created a demand for innovative technologies and methods that can deliver high performance and energy efficiency. In this work, we introduce a novel audio inference system that combines RAMAN, a Re-configurable and spArse tinyML Accelerator for infereNce, with a hardware-efficient Neuromorphic cochlea for pre-processing. The neuromorphic cochlea mimics human hearing, specifically by employing the 'Cascade of Asymmetric Resonators (CAR)' model to replicate the basilar membrane filter in the human cochlea. In this study, we utilize a 30 cascaded-filter cochlear section to process real-time audio data and a RAMAN classifier for audio classification. RAMAN leverages activation and weight sparsity within the neural network to reduce storage, latency, and power consumption. The proposed audio inference system has been implemented on a Microchip MPFS250T SoC field-programmable gate array (FPGA) with 52.57k LUTs, all while operating with a power consumption of 237.3 mW at 40 MHz clock frequency. The proposed audio inference system is designed for low-power auditory edge applications such as speaker verification, speech detection, and keyword spotting.