Georgios Venitourakis
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Georgios Venitourakis has a total of 17 co-authors. Here are the top co-authors by number of shared publications:
- Dionysios Reisis
Computer Center, Physics Department, National & Kapodistrian University of Athens, 15772 Athens, Greece
4 shared publications - Elissaios Alexios Papatheofanous
Electronics Lab, Physics Department, National and Kapodistrian University of Athens, Athens, Greece
2 shared publications - Alexandros Tsagkaropoulos
Electronics Lab, Physics Department, National & Kapodistrian University of Athens, 157 84 Athens, Greece
1 shared publication - Christoforos Kachris
Department of Electrical and Electronics Engineering, University of West Attica, 12241 Egaleo, Greece
1 shared publication - Christoforos Vasilakis
University of Athens
1 shared publication - Filippos Tziolos
Electronics Lab, Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece
1 shared publication
Latest Publications
- Open Access
- 6 June 2026
- Published by MDPI AG in Electronics
- Vol. 15 (12) , 2498
On the Edge-Computing-Oriented Inference of Radial Basis Function-Based Kolmogorov–Arnold Networks
The emerging Kolmogorov–Arnold networks (KANs) have set a new standard in machine learning (ML) tasks by prevailing over traditionally deployed multilayer perceptrons (MLPs) thanks to their enhanced interpretability through activation function learning, while they require increased computational complexity and memory footprint. Radial-basis function (RBF)-based KAN models maintain high performance over other variants of KANs with considerable size reduction and consequently more efficient execution. Aiming at effectively supporting the inference of RBF-KANs on Internet-of-Things (IoT) devices, this paper focuses on edge-oriented computing and introduces a soft intellectual property (IP) core, written in hardware description language (HDL), targeting the execution of such networks on all-programmable systems-on-chip (APSoC). The proposed design is fully pipelined and runtime configurable, allowing for real-time inference and latency-sensitive neural network deployment on-the-fly. A testbench reveals up to speedup when compared with a commercial edge central processing unit (CPU) and consumes considerably less power. The core’s adaptable design enables efficient allocation of resources and meets diverse throughput demands, making it well-suited for a broad range of IoT applications.- 6 June 2026
- Published by MDPI AG in Electronics
- Vol. 15 (12) , 2498
Open AccessOn the Edge-Computing-Oriented Inference of Radial Basis Function-Based Kolmogorov–Arnold Networks
The emerging Kolmogorov–Arnold networks (KANs) have set a new standard in machine learning (ML) tasks by prevailing over traditionally deployed multilayer perceptrons (MLPs) thanks to their enhanced interpretability through activation function learning, while they require increased computational complexity and memory footprint. Radial-basis function (RBF)-based KAN models maintain high performance over other variants of KANs with considerable size reduction and consequently more efficient execution. Aiming at effectively supporting the inference of RBF-KANs on Internet-of-Things (IoT) devices, this paper focuses on edge-oriented computing and introduces a soft intellectual property (IP) core, written in hardware description language (HDL), targeting the execution of such networks on all-programmable systems-on-chip (APSoC). The proposed design is fully pipelined and runtime configurable, allowing for real-time inference and latency-sensitive neural network deployment on-the-fly. A testbench reveals up to speedup when compared with a commercial edge central processing unit (CPU) and consumes considerably less power. The core’s adaptable design enables efficient allocation of resources and meets diverse throughput demands, making it well-suited for a broad range of IoT applications. - Open Access
- 18 November 2023
- Published by MDPI AG in Information
- Vol. 14 (11) , 617
Neural Network-Based Solar Irradiance Forecast for Edge Computing Devices
Aiming at effectively improving photovoltaic (PV) park operation and the stability of the electricity grid, the current paper addresses the design and development of a novel system achieving the short-term irradiance forecasting for the PV park area, which is the key factor for controlling the variations in the PV power production. First, it introduces the Xception long short-term memory (XceptionLSTM) cell tailored for recurrent neural networks (RNN). Second, it presents the novel irradiance forecasting model that consists of a sequence-to-sequence image regression NNs in the form of a spatio-temporal encoder–decoder including Xception layers in the spatial encoder, the novel XceptionLSTM in the temporal encoder and decoder and a multilayer perceptron in the spatial decoder. The proposed model achieves a forecast skill of 16.57% for a horizon of 5 min when compared to the persistence model. Moreover, the proposed model is designed for execution on edge computing devices and the real-time application of the inference on the Raspberry Pi 4 Model B 8 GB and the Raspberry Pi Zero 2W validates the results.
- 18 November 2023
- Published by MDPI AG in Information
- Vol. 14 (11) , 617
Open AccessNeural Network-Based Solar Irradiance Forecast for Edge Computing Devices
Aiming at effectively improving photovoltaic (PV) park operation and the stability of the electricity grid, the current paper addresses the design and development of a novel system achieving the short-term irradiance forecasting for the PV park area, which is the key factor for controlling the variations in the PV power production. First, it introduces the Xception long short-term memory (XceptionLSTM) cell tailored for recurrent neural networks (RNN). Second, it presents the novel irradiance forecasting model that consists of a sequence-to-sequence image regression NNs in the form of a spatio-temporal encoder–decoder including Xception layers in the spatial encoder, the novel XceptionLSTM in the temporal encoder and decoder and a multilayer perceptron in the spatial decoder. The proposed model achieves a forecast skill of 16.57% for a horizon of 5 min when compared to the persistence model. Moreover, the proposed model is designed for execution on edge computing devices and the real-time application of the inference on the Raspberry Pi 4 Model B 8 GB and the Raspberry Pi Zero 2W validates the results.
- Open Access
- 18 November 2022
- Published by MDPI AG in Electronics
- Vol. 11 (22) , 3794
Deep Learning-Based Image Regression for Short-Term Solar Irradiance Forecasting on the Edge
Photovoltaic (PV) power production is characterized by high variability due to short-term meteorological effects such as cloud movements. These effects have a significant impact on the incident solar irradiance in PV parks. In order to control PV park performance, researchers have focused on Computer Vision and Deep Learning approaches to perform short-term irradiance forecasting using sky images. Motivated by the task of improving PV park control, the current work introduces the Image Regression Module, which produces irradiance values from sky images using image processing methods and Convolutional Neural Networks (CNNs). With the objective of enhancing the performance of CNN models on the task of irradiance estimation and forecasting, we propose an image processing method based on sun localization. Our findings show that the proposed method can consistently improve the accuracy of irradiance values produced by all the CNN models of our study, reducing the Root Mean Square Error by up to 10.44 W/m for the MobileNetV2 model. These findings indicate that future applications which utilize CNNs for irradiance forecasting should identify the position of the sun in the image in order to produce more accurate irradiance values. Moreover, the integration of the proposed models on an edge-oriented Field-Programmable Gate Array (FPGA) towards a smart PV park for the real-time control of PV production emphasizes their advantages.- 18 November 2022
- Published by MDPI AG in Electronics
- Vol. 11 (22) , 3794
Open AccessDeep Learning-Based Image Regression for Short-Term Solar Irradiance Forecasting on the Edge
Photovoltaic (PV) power production is characterized by high variability due to short-term meteorological effects such as cloud movements. These effects have a significant impact on the incident solar irradiance in PV parks. In order to control PV park performance, researchers have focused on Computer Vision and Deep Learning approaches to perform short-term irradiance forecasting using sky images. Motivated by the task of improving PV park control, the current work introduces the Image Regression Module, which produces irradiance values from sky images using image processing methods and Convolutional Neural Networks (CNNs). With the objective of enhancing the performance of CNN models on the task of irradiance estimation and forecasting, we propose an image processing method based on sun localization. Our findings show that the proposed method can consistently improve the accuracy of irradiance values produced by all the CNN models of our study, reducing the Root Mean Square Error by up to 10.44 W/m for the MobileNetV2 model. These findings indicate that future applications which utilize CNNs for irradiance forecasting should identify the position of the sun in the image in order to produce more accurate irradiance values. Moreover, the integration of the proposed models on an edge-oriented Field-Programmable Gate Array (FPGA) towards a smart PV park for the real-time control of PV production emphasizes their advantages. - 3 October 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. abs 2006 1, 1-4
SoC FPGA Acceleration for Semantic Segmentation of Clouds in Satellite Images
During the past decades the reports produced by the satellites regarding the clouds became important for the weather prediction, the climate and lately for the generation and the distribution of the solar power. Image segmentation techniques produce these reports and they exploit convolutional neural networks (CNNs) to improve the accuracy results at the expense though of the requirements for execution time and hardware resources. Aiming at improving this trade-off the current work studies the design of a CNN model targeting the placement and the execution on a SoC FPGA while keeping the high accuracy. Moreover, it studies how to optimize the performance by distributing the execution of the CNN’s functions among the SoC’s IP cores and the SoC’s logic. The results are validated by using the Vitis-AI framework to implement the proposed CNN on a Xilinx Zynq UltraScale+ MPSoC ZCU104 board.
- 3 October 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. abs 2006 1, 1-4
SoC FPGA Acceleration for Semantic Segmentation of Clouds in Satellite Images
During the past decades the reports produced by the satellites regarding the clouds became important for the weather prediction, the climate and lately for the generation and the distribution of the solar power. Image segmentation techniques produce these reports and they exploit convolutional neural networks (CNNs) to improve the accuracy results at the expense though of the requirements for execution time and hardware resources. Aiming at improving this trade-off the current work studies the design of a CNN model targeting the placement and the execution on a SoC FPGA while keeping the high accuracy. Moreover, it studies how to optimize the performance by distributing the execution of the CNN’s functions among the SoC’s IP cores and the SoC’s logic. The results are validated by using the Vitis-AI framework to implement the proposed CNN on a Xilinx Zynq UltraScale+ MPSoC ZCU104 board.