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

arXiv: https://arxiv.org/abs/2101.04446 · HTML: https://mlsanity.com/paper/2101.04446/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 19 of 19)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Channel Pruning for Accelerating Very Deep Neural Networks](https://mlsanity.com/paper/1707.06168/) (2017)
- [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://mlsanity.com/paper/1606.06160/) (2016)
- [Fixed Point Quantization of Deep Convolutional Networks](https://mlsanity.com/paper/1511.06393/) (2015)
- [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://mlsanity.com/paper/1801.06601/) (2018)
- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network](https://mlsanity.com/paper/1901.02358/) (2019)
- [Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation](https://mlsanity.com/paper/1811.10413/) (2018)
- [PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors](https://mlsanity.com/paper/1908.11263/) (2019)
- [A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones](https://mlsanity.com/paper/1805.01831/) (2018)
- [FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things](https://mlsanity.com/paper/1911.03314/) (2019)
- [BinarEye: An Always-On Energy-Accuracy-Scalable Binary CNN Processor With All Memory On Chip in 28nm CMOS](https://mlsanity.com/paper/1804.05554/) (2018)
- [Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms](https://mlsanity.com/paper/2001.10876/) (2020)
- [Efficient Convolutional Neural Network For Audio Event Detection](https://mlsanity.com/paper/1709.09888/) (2017)
- [Additive Noise Annealing and Approximation Properties of Quantized Neural Networks](https://mlsanity.com/paper/1905.10452/) (2019)

## Built on by (most cited first, 10 of 10)

- [TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation](https://mlsanity.com/paper/2307.07813/) (2023)
- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices](https://mlsanity.com/paper/2103.03483/) (2021)
- [Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks](https://mlsanity.com/paper/2201.03386/) (2022)
- [Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devices](https://mlsanity.com/paper/2108.06128/) (2021)
- [Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications](https://mlsanity.com/paper/2311.07163/) (2023)
- [FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation](https://mlsanity.com/paper/2303.16322/) (2023)
- [MUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification](https://mlsanity.com/paper/2506.11331/) (2025)
- [Active Continual Learning with Metaplastic Binary Bayesian Neural Networks](https://mlsanity.com/paper/2605.30198/) (2026)
- [A Neuromorphic Trigger for Efficient Audio Event Detection](https://mlsanity.com/paper/2606.17775/) (2026)

## Abstract (arXiv)

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.