Optimizing Deep Learning Frameworks Using Bio-Inspired Algorithms

Deep learning (DL) model training must address the memory bottleneck to continue scaling. Processing-in-memory approaches can be a viable solution as they move computations near or into the memory, reducing substantial data movement. However, to deploy applications on such hardware, end-to-end software support is crucial for efficient computation mapping and scheduling as well as extensible code generation, but no consideration has been made for DL training workloads. In this paper, we propose XLA-NDP, a compiler and runtime solution for NDPX, a near-data processing (NDP) architecture integrated with an existing DL training framework. XLA-NDP offloads NDPX kernels and schedules them to overlap with GPU kernels to maximize parallelism based on GPU and NDPX costs, while providing a template-based code generator with low-level optimizations. The experiments showed that XLA-NDP provides up to 1.41x speedup (1.24x on average) over the GPU baseline for four DL model training.

Recently, deep learning has been exploited in the field of medical image analysis. However, the training of deep learning models with medical images is time-consuming since most medical image data are three-dimensional volumes or high-resolution two-dimensional images. Moreover, the optimization of numerous hyperparameters strongly affects the performance of deep learning. If a framework for training deep learning with hyperparameter optimization on a supercomputer system can be realized, it is expected to accelerate the training of deep learning with medical images. In this study, we described our novel environment for training deep learning with medical images on the supercomputer system in our institute (Reedbush-H supercomputer system) based on asynchronous parallel Bayesian optimization. We trained two types of automated lesion detection application in a constructed environment. The constructed environment enabled us to train deep learning with hyperparameter tuning in a short time.

This paper presents a Data Science-oriented application for image classification tasks that is able to automatically: a) gather images needed for training Deep Learning (DL) models with a built-in search engine crawler; b) remove duplicate images; c) sort images using built-in pre-trained DL models or user’s own trained DL model; d) apply data augmentation; e) train a DL classification model; f) evaluate the performance of a DL model and system by using an accuracy calculator as well as the Accuracy Per Consumption (APC), Accuracy Per Energy Cost (APEC), Time to closest APC (TTCAPC) and Time to closest APEC (TTCAPEC) metrics calculators. Experimental results show that the proposed Computer Vision application has several unique features and advantages, proving to be efficient regarding execution time and much easier to use when compared to similar applications.

Transient stability assessment (TSA) based on deep learning has recently attracted broad attention. The offline training of deep learning model typically requires a large number of labeled samples, and the training process is very time-consuming. Moreover, it is difficult to ensure that the trained model is suitable for all the operation conditions of power systems. In order to dramatically reduce the computation cost, and further enhance the adaptability of the model, an innovative method is proposed based on the active transfer learning (TL). First, the deep belief network (DBN) is designed as the base model and trained with a large number of labeled samples to obtain better evaluation performance for TSA. Second, when the topologies or operation conditions change substantially, active learning with information entropy is exploited to select the minimum number of the most informative samples, which effectively reduces the time of samples generation. Third, Maximum Mean Discrepancy (MMD) is calculated to select different transfer schemes. Only the last BP layer needs to be fine-tuned for the scenarios with a low MMD, but for those with high MMD, the entire network needs to be fine-tuned. The transfer time is greatly shortened on the premise of ensuring the transfer effect. The comprehensive experimental results of three power systems illustrate the effectiveness of the proposed method. Note to Practitioners—This research is motivated by automatic adaptive assessment of power system transient stability. The main objective is to realize online fast and continuous prediction with limited samples. This kind of demand is very normal in the industrial systems where it is expensive, time-consuming and difficult to obtain labeled samples. This paper proposes an active transfer learning method to select critical samples and update the deep learning model, which greatly reduces the time of samples generation and improves the updating efficiency of model. The proposed method is also applicable to make classification prediction or to address regression problems of industrial scenarios based on deep learning models.

Curious Containers: A framework for computational reproducibility in life sciences with support for Deep Learning applications

Robust perception systems allow farm robots to recognize weeds and vegetation, enabling the selective application of fertilizers and herbicides to mitigate the environmental impact of traditional agricultural practices. Today's perception systems typically rely on deep learning to interpret sensor data for tasks such as distinguishing soil, crops, and weeds. These approaches usually require substantial amounts of manually labeled training data, which is often time-consuming and requires domain expertise. This paper aims to reduce this limitation and propose an automated labeling pipeline for crop-weed semantic image segmentation in managed agricultural fields. It allows the training of deep learning models without or with only limited manual labeling of images. Our system uses RGB images recorded with unmanned aerial or ground robots operating in the field to produce semantic labels exploiting the field row structure for spatially consistent labeling. We use the rows previously detected to identify multiple crop rows, reducing labeling errors and improving consistency. We further reduce labeling errors by assigning an "unknown" class to challenging-to-segment vegetation. We use evidential deep learning because it provides predictions uncertainty estimates that we use to refine and improve our predictions. In this way, the evidential deep learning assigns high uncertainty to the weed class, as it is often less represented in the training data, allowing us to use the uncertainty to correct the semantic predictions. Experimental results suggest that our approach outperforms general-purpose labeling methods applied to crop fields by a large margin and domain-specific approaches on multiple fields and crop species. Using our generated labels to train deep learning models boosts our prediction performance on previously unseen fields with respect to unseen crop species, growth stages, or different lighting conditions. We obtain an IoU of 88.6% on crops, and 22.7% on weeds for a managed field of sugarbeets, where fully supervised methods have 83.4% on crops and 33.5% on weeds and other unsupervised domain-specific methods get 54.6% on crops and 11.2% on weeds. Finally, our method allows fine-tuning models trained in a fully supervised fashion to improve their performance in unseen field conditions up to +17.6% in mean IoU without additional manual labeling.

In this work we propose a workflow to deal with overlaid images—images with superimposed text and company logos—, which is very common in underwater monitoring videos and surveillance camera footage. It is demonstrated that it is possible to use Explaining Artificial Intelligence to improve deep learning models performance for image classification tasks in general. A deep learning model trained to classify metal surface defect, which previously had a low performance, is then evaluated with Layer-wise relevance propagation—an Explaining Artificial Intelligence technique—to identify problems in a dataset that hinder the training of deep learning models in a wide range of applications. Thereafter, it is possible to remove this unwanted information from the dataset—using different approaches: from cutting part of the images to training a Generative Inpainting neural network model—and retrain the model with the new preprocessed images. This proposed methodology improved F1 score in 20% when compared to the original trained dataset, validating the proposed workflow.

In the case of neonates, especially low birth weight preterm and high-risk infants, portable X-rays are frequently used. However, the image quality of portable X-rays is significantly lower compared to standard adult or pediatric X-rays, leading to considerable challenges in identifying abnormalities. Although attempts have been made to introduce deep learning to address these image quality issues, the poor quality of the images themselves hinders the training of deep learning models, further emphasizing the need for image enhancement. Additionally, since neonates have a high cell division rate and are highly sensitive to radiation, increasing radiation exposure to improve image quality is not a viable solution. Therefore, it is crucial to enhance image quality through preprocessing before training deep learning models. While various image enhancement methods have been proposed, Contrast Limited Adaptive Histogram Equalization (CLAHE) has been recognized as an effective technique for contrast-based image improvement. However, despite extensive research, the process of setting CLAHE’s hyperparameters still relies on a brute force, manual approach, making it inefficient. To address this issue, we propose a method called Bayesian Optimization CLAHE(BO-CLAHE), which leverages Bayesian optimization to automatically select the optimal hyperparameters for X-ray images used in diagnosing lung diseases in preterm and high-risk neonates. The images enhanced by BO-CLAHE demonstrated superior performance across several classification models, with particularly notable improvements in diagnosing Transient Tachypnea of the Newborn (TTN). This approach not only reduces radiation exposure but also contributes to the development of AI-based diagnostic tools, playing a crucial role in the early diagnosis and treatment of preterm and high-risk neonates.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

Artificial intelligence (AI) has the ability to predict rheological properties and constituent composition of 3D-printed materials with appropriately trained models. However, these models are not currently available for use. In this work, we trained deep learning (DL) models to (1) predict the rheological properties, such as the storage (G’) and loss (G”) moduli, of 3D-printed polyacrylamide (PAA) substrates, and (2) predict the composition of materials and associated 3D printing parameters for a desired pair of G’ and G”. We employed a multilayer perceptron (MLP) and successfully predicted G’ and G” from seven gel constituent parameters in a multivariate regression process. We used a grid-search algorithm along with 10-fold cross validation to tune the hyperparameters of the MLP, and found the R2 value to be 0.89. Next, we adopted two generative DL models named variational autoencoder (VAE) and conditional variational autoencoder (CVAE) to learn data patterns and generate constituent compositions. With these generative models, we produced synthetic data with the same statistical distribution as the real data of actual hydrogel fabrication, which was then validated using Student’s t-test and an autoencoder (AE) anomaly detector. We found that none of the seven generated gel constituents were significantly different from the real data. Our trained DL models were successful in mapping the input–output relationship for the 3D-printed hydrogel substrates, which can predict multiple variables from a handful of input variables and vice versa.

Deep learning has proven to be a powerful approach to solving challenging problems, ranging from natural language processing, speech recognition, to computer vision. The proposed model has capable of matching the expanding amount of facts as well as complexity of the algorithms is demands of deep researching methodologies. These demands cannot be met with traditional computing environments. This is why cloud computing technologies have developed, offering a scalable and affordable alternative for executing deep learning algorithms. Cloud computing platforms provide the resources necessary to run deep learning workloads including compute, storage, and networking. This means you no longer have to spend lots of money on expensive hardware and makes it easier for teams and researchers to deploy and train deep learning models faster. Cloud computing has ushered in a new era of deep learning developments by mobilizing the power of specialized hardware, notably GPUs, to accelerate the training and performance of deep learning models.

In an era marked by the escalating architectural complexity of the Internet, network intrusion detection stands as a pivotal element in cybersecurity. This paper introduces Learn-IDS, an innovative framework crafted to bridge existing gaps between datasets and the training process within deep learning (DL) models for Network Intrusion Detection Systems (NIDS). To elevate conventional DL-based NIDS methods, which are frequently challenged by the evolving cyber threat landscape and exhibit limited generalizability across various environments, Learn-IDS works as a potent and adaptable platform and effectively tackles the challenges associated with datasets used in deep learning model training. Learn-IDS takes advantage of the raw data to address three challenges of existing published datasets, which are (1) the provided tabular format is not suitable for the diversity of DL models; (2) the fixed traffic instances are not suitable for the dynamic network scenarios; (3) the isolated published datasets cannot meet the cross-dataset requirement of DL-based NIDS studies. The data processing results illustrate that the proposed framework can correctly process and label the raw data with an average of 90% accuracy across three published datasets. To demonstrate how to use Learn-IDS for a DL-based NIDS study, we present two simple case studies. The case study on cross-dataset sampling function reports an average of 30.3% OOD accuracy improvement. The case study on data formatting function shows that introducing temporal information can enhance the detection accuracy by 4.1%.The experimental results illustrate that the proposed framework, through the synergistic fusion of datasets and DL models, not only enhances detection precision but also dynamically adapts to emerging threats within complex scenarios.

Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning-based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre-processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided. This review strives to provide surgeons with the groundwork needed to identify gaps in the clinical landscape that deep learning models may be able to fill and equips them with the knowledge needed to lead an interdisciplinary team through the process of creating novel deep-learning-based solutions to fill those gaps.

Accurate rainfall forecasting is crucial in activities such as agricultural planning, water resource management, and flood preparedness, so failure to accurately forecast rainfall might undermine these activities. This study aimed to improve the accuracy of daily rainfall prediction in Hai district, Kilimanjaro region, Tanzania, a region characterised by a bimodal rainfall pattern, by comparing rainfall prediction accuracies of Traditional Machine Learning (TML) and Deep Learning (DL) models. The 25-year daily rainfall dataset for Hai district was first pre-processed by cleaning, normalisation, and splitting into training (first 60%), validation (next 20%), and test (last 20%) sets. TML models (K-Nearest Neighbours (KNN) through its variant KNN Regressor and Support Vector Machine (SVM) through its variant Support Vector Regressor (SVR)) were developed, trained, and their performances compared with developed and trained DL models (Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)). Each model’s test performance was evaluated using the Root Mean Squared Error (RMSE) metric. The findings revealed that the DL models, LSTM and GRU, achieved test RMSE scores of 6.35 mm and 6.49 mm, respectively, while the TML models, KNN and SVM, achieved test RMSE scores of 6.61 mm and 10.42 mm, respectively. The study demonstrated that DL models are more effective for accurate rainfall forecasting than TML models in regions with complex rainfall patterns. The insights gained will guide the development of more reliable, location-specific weather prediction systems, ultimately supporting informed decision-making in agriculture, environmental management, and flood risk reduction in Tanzania and other regions facing similar climatic challenges

Deep Learning (DL) has seen rapid adoption in all domains. Since training DL models is expensive, both in terms of time and resources, application workflows that make use of DL increasingly need to operate with a large number of derived learning models, which are obtained through transfer learning and fine-tuning. At scale, thousands of such derived DL models are accessed concurrently by a large number of processes. In this context, an important question is how to design and develop specialized DL model repositories that remain scalable under concurrent access, while addressing key challenges: how to query the DL model architectures for specific patterns? How to load/store a subset of layers/tensors from a DL model? How to efficiently share unmodified layers/tensors between DL models derived from each other through transfer learning? How to maintain provenance and answer ancestry queries? State of art leaves a gap regarding these challenges. To fill this gap, we introduce EvoStore, a distributed DL model repository with scalable data and metadata support to store and access derived DL models efficiently. Large-scale experiments on hundreds of GPUs show significant benefits over state-of-art with respect to I/O and metadata performance, as well as storage space utilization.