1. Reliability improvements for in-wheel motorGašper Petelin, Rok Hribar, Stane Ciglarič, Jernej Herman, Anton Biasizzo, Peter Korošec, Gregor Papa, 2024, independent scientific component part or a chapter in a monograph Abstract: Setting up a reliable electric propulsion system in the automotive sector requires an intelligent condition monitoring device capable of reliably assessing the state and the health of the electric motor. To allow for a massive integration of such monitoring devices, they must be inexpensive and small. These requirements limit their accuracy. However, we show in this chapter that these limitations can be significantly reduced by appropriate processing of the sensor data. We have used machine learning models (random forest and XGBoost) to transform very noisy motor winding insulation resistance measurements made by a low-cost device into a much more reliable value that can compete with measurements made by a high-priced state-of-the-art measurement system. The proposed method is an important building block for a future smart condition monitoring system and enables a cost-effective and accurate assessment of the condition of electric motor health in connection with the condition of their winding insulation.
Keywords: machine learning models, low-cost device, electric motor
Published in DiRROS: 23.07.2024; Views: 207; Downloads: 82
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2. Towards deploying highly quantized neural networks on FPGA using chiselJure Vreča, Anton Biasizzo, 2023, published scientific conference contribution Abstract: We present chisel4ml, a Chisel-based tool that generates hardware for highly quantized neural networks described in QKeras. Such networks typically use parameters with bitwidths less than 8 bits and may have pruned connections. Chisel4ml can generate the highly quantized neural network as a single combinational circuit with pipeline registers in between the different layers. It supports heterogeneous quantization where each layer can have a different precision. The full parallelization enables very low-latency and high throughput inference, that are required for certain tasks. We illustrate this on the triggering system for the CERN Large Hadron Collider, which filters out events of interest and sends them on for further processing. We compare our tool against hls4ml, a high-level synthesis based approach for deploying similar neural networks. Chisel4ml is still under development. However, it already achieves comparable results to hls4ml for some neural network architectures. Chisel4ml is available on https://github.com/cs-jsi/chisel4ml.
Keywords: neural networks, QKeras, Chisel4ml
Published in DiRROS: 23.04.2024; Views: 317; Downloads: 204
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3. Hardware–software co-design of an audio feature extraction pipeline for machine learning applicationsJure Vreča, Ratko Pilipović, Anton Biasizzo, 2024, original scientific article Abstract: Keyword spotting is an important part of modern speech recognition pipelines. Typical contemporary keyword-spotting systems are based on Mel-Frequency Cepstral Coefficient (MFCC) audio features, which are relatively complex to compute. Considering the always-on nature of many keyword-spotting systems, it is prudent to optimize this part of the detection pipeline. We explore the simplifications of the MFCC audio features and derive a simplified version that can be more easily used in embedded applications. Additionally, we implement a hardware generator that generates an appropriate hardware pipeline for the simplified audio feature extraction. Using Chisel4ml framework, we integrate hardware generators into Python-based Keras framework, which facilitates the training process of the machine learning models using our simplified audio features.
Keywords: FPGA, MFCC, keyword spotting, chisel
Published in DiRROS: 25.03.2024; Views: 522; Downloads: 473
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