The primary objective of this paper is to propose a multi-objective optimization process of an IoT network for cattle monitoring in open environments. The proposed approach utilizes a LoRa network simulation model and optimization algorithms focused on improving the choice of its configuration parameters. It simulates cattle's real displacement and compares the optimization performance of three algorithms, indicating the best of these. Results demonstrate that the approach used in the work successfully optimizes the parameters, reducing energy consumption and increasing communication between gateways in this application scenario.
Field-Programmable Gate Arrays (FPGAs) have emerged as a promising platform for accelerating AI applications in IoT devices. In this context, Spiking Neural Networks (SNNs) have demonstrated superior energy efficiency compared to traditional artificial neural networks when implemented on FPGAs. Although various frameworks have been proposed for mapping SNNs onto FPGAs, none of them have leveraged High-Level Synthesis (HLS), which facilitates faster development cycles and greater design transparency. This work introduces NeuroHLS, an HLS-based framework for implementing SNNs on FPGAs. NeuroHLS offers both resource- and latency-optimized implementation strategies, and supports fine-grained parameter customization at the layer level. To validate the framework, a 784128-10 SNN was implemented and evaluated on the N-MNIST dataset. Performance was benchmarked against existing state-of-the-art frameworks. Experimental results demonstrate that both implementation strategies offered by NeuroHLS significantly outperform existing approaches in terms of inference latency and energy efficiency. In particular, one configuration achieved an inference latency of 0.8us and an average energy consumption of 2.16uJ per inference.
The deployment of Deep Neural Networks (DNNs) in safety-critical systems necessitates resilience against hardware threats like the Bit-Flip Attack (BFA). This challenge becomes particularly nuanced for quantized networks. Unlike their floating-point counterparts where single flips can be catastrophic, the inherent robustness of quantized models—the exclusive focus of this work—makes identifying critical bits computationally prohibitive. We introduce the Machine Learning-Guided Framework for Efficient BFAs (MLBFA), a novel framework that leverages Machine Learning (ML) to execute highly efficient Bit-Flip Attacks (BFAs). MLBFA operates in three stages: (1) it builds a Vulnerability Model (VM) from a limited statistical fault injection campaign using a multidimensional feature set; (2) it uses the VM to predict and rank the vulnerability of all parameters across the network; and (3) it exploits this ranking to guide advanced search algorithms (Greedy, Evolutionary, and Progressive) in identifying the minimal set of bit-flips required to neutralize the DNN. On quantized ResNet architectures, crucial for embedded systems, MLBFA consistently outperforms state-of-the-art methods, neutralizing a ResNet-32 with only six bit-flips and reducing the required flips by nearly 50% on deeper models like the ResNet-56. This work demonstrates that a holistic, ML-guided approach provides a superior strategic map for executing more potent hardware-level attacks with fewer resources.
Deploying deep learning models in embedded agricultural systems requires balancing predictive performance with strict hardware constraints such as memory, power, and latency. This work uses quantization techniques, specifically post-training quantization (PTQ) and quantization-aware training (QAT), to optimize convolutional neural networks for real-time pest detection in smart traps. Using the Brevitas framework for quantization and FINN for hardware generation targeting FPGAs, we evaluate a range of weight and activation bit-width configurations. Our results show that QAT significantly outperforms PTQ, particularly in aggressive low-bit scenarios, achieving high accuracy while drastically reducing hardware resource utilization. Among the proposed solutions, one achieves 87.47% accuracy while using less than 10% of the LUTs required by its full-precision counterpart. Comparative analysis with standard models such as ResNet18 and MobileNet further validates the effectiveness of our approach. This study highlights the practicality of QAT-driven quantization for edge Artificial Intelligence applications in agriculture. It paves the way for future work, including power, latency, and throughput profiling, to support large-scale deployment.
Semantic segmentation of 3D point clouds plays a fundamental role in applications such as autonomous vehicles, robotics, and 3D mapping, enabling the precise identification of objects and relevant classes. However, performing this task in real time presents challenges related to computational efficiency and model optimization. This work presents the development and performance analysis of a pipeline for real-time Light Detection and Ranging (LiDAR ) data acquisition and inference, integrating machine learning models based on Convolutional Neural Networks (CNNs). The proposed approach combines Software-in-the-Loop techniques and the concept of a digital twin, covering the entire process from data capture and preprocessing to semantic segmentation, ensuring a continuous processing workflow. The experiments done with an adaptation of the RIU-Net architecture demonstrated the feasibility of the proposed solution, showing satisfactory results in terms of execution time, accuracy, and Intersection over Union (IoU), reinforcing its applicability to real-time 3D computer vision systems.
