Ali Dabbous

Tiny machine learning technologies are bringing intelligence ever closer to the sensor, thus enabling the key benefits of edge computing (e.g., reduced latency, improved data security, higher energy efficiency, and lower bandwidth consumption, also without the need for constant connectivity). This promises to significantly enhance industrial applications but requires suited development tools to deal with the complexity of the edge technologies and context. We propose an agile Jupyter Python notebook as a simple, manageable tool to efficiently and effectively develop microcontroller-based intelligent imaging classification sensors. The notebook implements a methodology involving hyperparameter tuning and comparison of different shallow and deep learning models, with quantization. It exports TensorFlow Lite models, deployable on several microcontroller families, and optionally exploits the STM32Cube.AI Developer Cloud service, which allows benchmarking the developed models on a set of real-world tiny hardware target platforms. Assessment concerns various types of metrics, both for machine learning (e.g., accuracy) and embedded systems (e.g., memory footprint, latency, energy consumption). We have verified the support for development effectiveness and efficiency on four ultra-low resolution image-classification datasets, with different levels of input and task complexity. In all cases, the tool was able to build microcontroller-deployment-ready, beyond the state-of-the-art models, within one hour on Google Colab CPUs.

Structural health monitoring is key in civil engineering because of the importance and the aging of the infrastructure. We argue that applying leading-edge, data-driven methods of large-scale complex industrial systems may be beneficial, particularly for accuracy and responsiveness. A fundamental step concerns the identification of the best tools to extract meaningful information from the vibrational raw signals. To this end, we study the application of two convolutional neural network architectures that have emerged in literature for efficient feature extraction from time-series, namely WaveNet and MiniRocket. The test bench is the Z24 Bridge progressive damage test (PDT) classification dataset. Results show that a model based on WaveNet reaches state-of-the-art performance, also reducing model size and computational complexity. WaveNet proves perfectly suited to interpret the bridge vibration waveforms directly in the time domain, without any specific preprocessing. On the other hand, MiniRocket excels for ease of configuration (only two hyperparameters are to be tweaked), overall training efficiency, and model size, lending itself as a valuable agile alternative (e.g., for rapid prototyping). Our main advancement is thus the identification and characterization of highly effective feature extraction methods, employable in different SHM tasks. We have assessed the performance of the models on two embedded platforms, proposing a smart sensor system where a local hub collects the signals from a constellation of inertial sensors and infers damage assessment onsite, allowing the bridge to self-assess its health state without resorting to connectivity nor cloud resources.

Binarization is an extreme quantization technique that is attracting research in the Internet of Things (IoT) field, as it radically reduces the memory footprint of deep neural networks without a correspondingly significant accuracy drop. To support the effective deployment of Binarized Neural Networks (BNNs), we propose CBin-NN, a library of layer operators that allows the building of simple yet flexible convolutional neural networks (CNNs) with binary weights and activations. CBin-NN is platform-independent and is thus portable to virtually any software-programmable device. Experimental analysis on the CIFAR-10 dataset shows that our library, compared to a set of state-of-the-art inference engines, speeds up inference by 3.6 times and reduces the memory required to store model weights and activations by 7.5 times and 28 times, respectively, at the cost of slightly lower accuracy (2.5%). An ablation study stresses the importance of a Quantized Input Quantized Kernel Convolution layer to improve accuracy and reduce latency at the cost of a slight increase in model size.

Datasets are key to developing new machine learning-based applications but are very costly to prepare, which hinders research and development in the field. We propose an edge-to-cloud end-to-end system architecture optimized for sport activity recognition dataset collection and application deployment. Tests in authentic contexts of use in four different sports have revealed the system’s ability to effectively collect machine learning-usable data, with an energy consumption compatible with the timeframe of most of the sport types. The proposed architecture relies on a key feature of the Measurify internet of things framework for the management of measurement data (i.e., .csv dataset management) and supports a workflow designed for efficient data labeling of signal timeseries. The architecture is independent of any specific sport, and a new dataset generation application can be set up in a few days, even by novice developers. With a view to concretely supporting the R&D community, our work is released open-source.