Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices

Sound event detection (SED) is a hot topic in consumer and smart city applications. Existing approaches based on Deep Neural Networks are very effective, but highly demanding in terms of memory, power, and throughput when targeting ultra-low power always-on devices. Latency, availability, cost, and privacy requirements are pushing recent IoT systems to process the data on the node, close to the sensor, with a very limited energy supply, and tight constraints on the memory size and processing capabilities precluding to run state-of-the-art DNNs. In this paper, we explore the combination of extreme quantization to a small-footprint binary neural network (BNN) with the highly energy-efficient, RISC-V-based (8+1)-core GAP8 microcontroller. Starting from an existing CNN for SED whose footprint (815 kB) exceeds the 512 kB of memory available on our platform, we retrain the network using binary filters and activations to match these memory constraints. (Fully) binary neural networks come with a natural drop in accuracy of 12-18% on the challenging ImageNet object recognition challenge compared to their equivalent full-precision baselines. This BNN reaches a 77.9% accuracy, just 7% lower than the full-precision version, with 58 kB (7.2 times less) for the weights and 262 kB (2.4 times less) memory in total. With our BNN implementation, we reach a peak throughput of 4.6 GMAC/s and 1.5 GMAC/s over the full network, including preprocessing with Mel bins, which corresponds to an efficiency of 67.1 GMAC/s/W and 31.3 GMAC/s/W, respectively. Compared to the performance of an ARM Cortex-M4 implementation, our system has a 10.3 times faster execution time and a 51.1 times higher energy-efficiency.Comment: 6 pages conferenc

Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms

Outdoor acoustic events detection is an exciting research field but challenged by the need for complex algorithms and deep learning techniques, typically requiring many computational, memory, and energy resources. This challenge discourages IoT implementation, where an efficient use of resources is required. However, current embedded technologies and microcontrollers have increased their capabilities without penalizing energy efficiency. This paper addresses the application of sound event detection at the edge, by optimizing deep learning techniques on resource-constrained embedded platforms for the IoT. The contribution is two-fold: firstly, a two-stage student-teacher approach is presented to make state-of-the-art neural networks for sound event detection fit on current microcontrollers; secondly, we test our approach on an ARM Cortex M4, particularly focusing on issues related to 8-bits quantization. Our embedded implementation can achieve 68% accuracy in recognition on Urbansound8k, not far from state-of-the-art performance, with an inference time of 125 ms for each second of the audio stream, and power consumption of 5.5 mW in just 34.3 kB of RAM

Evaluation of Pre-Trained CNN Models for Cardiovascular Disease Classification: A Benchmark Study

In this paper, we present an up-to-date benchmarking of the most commonly used pre-trained CNN models using a merged set of three available public datasets to have a large enough sample range. From the 18th century up to the present day, cardiovascular diseases, which are considered among the most significant health risks globally, have been diagnosed by the auscultation of heart sounds using a stethoscope. This method is elusive, and a highly experienced physician is required to master it. Artificial intelligence and, subsequently, machine learning are being applied to equip modern medicine with powerful tools to improve medical diagnoses. Image and audio pre-trained convolution neural network (CNN) models have been used for classifying normal and abnormal heartbeats using phonocardiogram signals. We objectively benchmark more than two dozen image-pre-trained CNN models in addition to two of the most popular audio-based pre-trained CNN models: VGGish and YAMnet, which have been developed specifically for audio classification. The experimental results have shown that audio-based models are among the best- performing models. In particular, the VGGish model had the highest average validation accuracy and average true positive rate of 87% and 85%, respectively

Neural network distillation on IoT platforms for sound event detection

In most classification tasks, wide and deep neural networks perform and generalize better than their smaller counterparts, in particular when they are exposed to large and heterogeneous training sets. However, in the emerging field of Internet of Things memory footprint and energy budget pose severe limits on the size and complexity of the neural models that can be implemented on embedded devices. The Student-Teacher approach is an attractive strategy to distill knowledge from a large network into smaller ones, that can fit on low-energy low-complexity embedded IoT platforms. In this paper, we consider the outdoor sound event detection task as a use case. Building upon the VGGish network, we investigate different distillation strategies to substantially reduce the classifier's size and computational cost with minimal performance losses. Experiments on the UrbanSound8K dataset show that extreme compression factors (up to 4.2 · 10−4 for parameters and 1.2 · 10−3 for operations with respect to VGGish) can be achieved, limiting the accuracy degradation from 75% to 70%. Finally, we compare different embedded platforms to analyze the trade-off between available resources and achievable accuracy

Neural Network Distillation on IoT Platforms for Sound Event Detection

In most classification tasks, wide and deep neural networks perform and generalize better than their smaller counterparts, in particular when they are exposed to large and heterogeneous training sets. However, in the emerging field of Internet of Things memory footprint and energy budget pose severe limits on the size and complexity of the neural models that can be implemented on embedded devices. The Student-Teacher approach is an attractive strategy to distill knowledge from a large network into smaller ones, that can fit on low-energy low-complexity embedded IoT platforms. In this paper, we consider the outdoor sound event detection task as a use case. Building upon the VGGish network, we investigate different distillation strategies to substantially reduce the classifier's size and computational cost with minimal performance losses. Experiments on the UrbanSound8K dataset show that extreme compression factors can be achieved, limiting the accuracy degradation from 75% to 70%. Finally, we compare different embedded platforms to analyze the trade-off between available resources and achievable accuracy