On-chip learning enables machine learning models to be trained or updated directly on specialized hardware rather than on external CPUs or GPUs, offering lower latency, improved energy-efficiency, enhanced privacy, and real-time adaptability for edge devices. In Spiking Neural Networks (SNNs), this capability relies on dynamic synaptic weight adaptation, but such adaptability also introduces significant security risks. In this work, we demonstrate a power side-channel attack on a quantized SNN implemented on a CW305 FPGA platform using ChipWhisperer. Our analysis identifies consistent power leakage patterns associated with neuron update operations, allowing an attacker to infer internal model attributes without direct access to the model’s weights or inputs. We further perform Correlation Power Analysis (CPA) with a Hamming Weight leakage model to recover secret synaptic weights with high confidence using as few as 1,500 power traces. These results expose critical vulnerabilities in on-chip learning systems and SNN architectures, highlight realistic threats to IoT and edge applications, and motivate mitigation strategies at the software-hardware boundary, including secure design practices, cryptographic protections, and access control mechanisms, without significantly degrading performance.

This research addresses the challenges faced by firmware developers, security researchers, and enthusiasts who work with low-level microcontroller (MCU) documentation, which often spans hundreds of complex pages. Current structured approaches, such as System View Description (SVD), are widely used but suffer from manual, labor-intensive creation processes and inconsistent vendor adherence to CMSIS-SVD standards. We propose an automated solution using Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG), capable of effectively parsing and extracting structured information, including text, tables, and images from MCU reference manuals/ datasheets. To mitigate hallucination issues inherent in LLMs, we fine-tuned models using a dataset derived from CMSIS-SVD files, which we will open-source for community benefit. We also experimented with few-shot models. Additionally, we developed a standardized structured ontology that is automatically populated with information extracted through LLM assistance from the reference manuals of the corresponding MCUs. Our approach was evaluated using OpenAI's GPT-4o under one-shot, few-shot, and fine-tuning scenarios, all incorporating RAG. We also experimented with the open-source LLM model CodeLlama. The results highlight substantial improvements in automatically extracting peripheral details and information from MCU reference manuals. Thus, it helps reduce manual effort and time. The key contribution of our work lies in the tailored adaptation of existing AI techniques to address the specific challenges of embedded systems documentation. We perform standardized ontology creation and multimodal parsing. We leverage RAG with MCU-specific finetuning and few-shot learning to generate structured information from hundreds of pages of MCU documentation. This opens the door to potential applications such as more accurate firmware code generation and reverse engineering for security analysis.

The growing complexity of modern computing frameworks has led to an increase in cybersecurity vulnerabilities reported to the National Vulnerability Database (NVD). Extracting meaningful trends from this vast amount of unstructured data is challenging without proper tools and methodologies. Existing approaches lack a holistic strategy for vulnerability mitigation and prediction and effective knowledge extraction from the Common Weakness Enumeration (CWE), Common Vulnerability Exposure (CVE), and Common Attack Pattern Enumeration and Classification (CAPEC) databases. We introduce the AI-enabled Hardware Weakness and Risk Exploration and Storytelling Framework with LLM-assisted Mitigation Suggestion (HWREx), designed to address hardware vulnerabilities and IoT security. Our architecture features an Ontology-driven Storytelling capability that automates ontology updates to track vulnerability patterns and evolution over time, while offering mitigation strategies. It also clarifies the complex interrelations among CVEs, CWEs, and CAPECs through interactive visual knowledge graphs. Our framework achieved accuracy rates of 62% for CWE-CWE, 83% for CWE-CVE, and 77% for CWE-CAPEC linkage predictions. These graphs are instrumental for in-depth hardware weakness analysis and enable HWREx to deliver comprehensive assessments and actionable mitigation strategies. Additionally, HWREx utilizes Generative Pre-trained Transformers (GPT) to offer tailored mitigation suggestions.

This research-to-practice paper presents a novel pedagogical tool for hardware cybersecurity education and workforce development. The growing importance of hardware security has made it essential for individuals and organizations to understand hardware security principles and best practices. However, the current educational curriculum falls short of fulfilling these emerging demands due to the rapidly changing hardware security landscape and limited opportunities for hands-on training. To address these challenges, we propose and have developed the Interactive Hardware and Cybersecurity (I-HaC) Educational Framework, a pedagogical educational framework that supplements existing courses by leveraging generative AI for individualized instruction related to hardware and cybersecurity, data mining, and applied Machine Learning (ML), as well as data visualization to enhance cybersecurity education and workforce development. The framework is designed to be utilized by graduate and undergraduate Electrical and Computer Engineering (ECE) and Computer Science (CS) students for a comprehensive introduction to cybersecurity exploits and countermeasures in an interactive manner with hands-on components. Using I-HaC, we have developed tailored lab components for a diverse range of students and intend to release I-HaC as open-source for the benefit of the ECE and CS education community.

There is significant demand and urgency to prepare electrical and computer engineering students regarding the operational and performance characteristics of machine learning (ML) hardware accelerators. Convolutional Neural Networks (CNNs), which are utilized for real-time and large dataset image classification tasks, are appropriate targets for hardware acceleration. Designing accelerators for CNNs necessitates understanding the manipulation of CNN parameters. We introduce a hands-on pedagogy whereby learners can identify, modify, and appreciate the interaction of the CNN parameters within an interactive GUI. CASCADE (Computer Aided Student's CNN Analyzer for Design Exploration), a simulation-based framework for Design Space Exploration (DSE) of CNN FPGA-based accelerators is developed, including datapath synthesis, simulation, training, and testbench steps. We offer a case study of High-Level Synthesis (HLS) based CNN implementations targeting the MNIST dataset and present simulation results, namely hardware utilization, accuracy, and operating frequency, and offer insight into potential design trade-offs facing modern engineers.

Electricity demand prediction is crucial to ensure the operational safety and cost-efficient operation of the power system. Electricity demand has predominantly been predicted deterministically, while uncertainty analysis has been usually overlooked. To address this research gap, an integrated Neural Facebook Prophet (NFBP) model and Gaussian Kernel Density Estimation (KDE) model is proposed in this paper, as a way to obtain point and interval predictions of electricity demand, quantifying this way the uncertainty in the predictions. First, historical lagged data, created by utilizing the Partial Auto-correlation Function and Mutual Information Test, is applied to train a prediction model based on NFBP, Deep Learning (DL) as well as Statistical Models. Second, the model Prediction Errors (PE) are derived from the difference between actual and predicted values. A splitting strategy based on the mean and standard deviation of PE is proposed. Finally, electricity demand prediction intervals are obtained by applying Gaussian KDE on split PE. To verify the effectiveness of the proposed model, simulation studies are carried out for three prediction horizons on freely available datasets for the Bulimba sub-station in Southeast Queensland, Australia. Compared with DL models (Long-Short Term Memory Network and Deep Neural Network), the Root Mean Square Error of the NFBP model was reduced by 6.1% and 11.3% for 0.5-hr ahead, 22.7% and 26.3% for 6-hr ahead, and 31.8% and 29.9% for daily prediction. In addition, the Prediction Interval normalized Interval width is smaller in magnitude for the proposed NFBP-KDE model compared to other DL and Statistical models

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with Support Vector Regression (SVR) approach. First, the CNN algorithm is used to extract local patterns as well as common features that occur recurrently in time series data at different intervals. Then, the SVR is subsequently adopted to replace the fully connected CNN layers to predict the daily GSR time series data at six solar farms in Queensland, Australia. To develop the hybrid CSVR model, we adopt the most pertinent meteorological variables from Global Climate Model and Scientific Information for Landowners database. From a pool of Global Climate Models variables and ground-based observations, the optimal features are selected through a metaheuristic Feature Selection algorithm, an Atom Search Optimization method. The hyperparameters of the proposed CSVR are optimized by mean of the HyperOpt method, and the overall performance of the objective algorithm is benchmarked against eight alternative DL methods, and some of the other Machine Learning approaches (LSTM, DBN, RBF, BRF, MARS, WKNNR, GPML and M5TREE) methods. The results obtained shows that the proposed CSVR model can offer several predictive advantages over the alternative DL models, as well as the conventional ML models. Specifically, we note that the CSVR model recorded a root mean square error/mean absolute error ranging between ≈ 2.172–3.305 MJ m2/1.624–2.370 MJ m2 over the six tested solar farms compared to ≈ 2.514–3.879 MJ m2/1.939–2.866 MJ m2 from alternative ML and DL algorithms. Consistent with this predicted error, the correlation between the measured and the predicted GSR, including the Willmott’s, Nash-Sutcliffe’s coefficient and Legates & McCabe’s Index was relatively higher for the proposed CSVR model compared to other DL and Machine Learning methods for all of the study sites. Accordingly, this study advocates the merits of CSVR model to provide a viable alternative to accurately predict GSR for renewable energy exploitation, energy demand or other forecasting-based applications.