Ezekiel, Aniebiet Micheal
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Ezekiel, Aniebiet Micheal
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Publication Access status: Open Access , Distributed Time-Triggered Caching and Memory Access Optimisation for Neural Network Tensor Accelerators in Multicore Safety-Critical Systems(2025)Neural network accelerators are essential for meeting the computational demands of modern AI applications; however, their use in safety-critical and real-time environments presents significant challenges, primarily due to inefficiencies in memory access and interference from other applications, leading to unpredictable memory access patterns. This dissertation addresses these memory access bottlenecks by proposing a time-triggered architecture that enhances the memory access mechanisms of tensor accelerators. Traditional accelerators, such as the Versatile Tensor Accelerator (VTA), encounter limitations related to memory bandwidth, resource contention, and variable latency, which impair performance in safety-critical, memory-intensive tasks. This work introduces the Time-Triggered Memory Access VTA (TTmaVTA), which applies time-triggered architectures to control and regulate the memory access of the VTA, ensuring predictable and conflict-free memory transactions. The TTmaVTA framework is further refined with dual memory optimisation techniques, prefetching, and caching mechanisms. These enhancements, collectively referred to as OPTTmaVTA, improve memory throughput while significantly reducing memory access latency. Prefetching mechanisms retrieve data during idle memory cycles, minimising delays due to dependency stalls, while deterministic caching optimises frequently accessed memory operations, reducing memory bus accesses. Together, these methods improve the memory performance of neural network accelerators while ensuring timing predictability, particularly in safety-critical contexts. This dissertation presents hardware experiments and software simulations that validate the effectiveness of TTmaVTA and OPTTmaVTA in improving memory access predictability and memory throughput. Hardware-based experiments using a Conv2D workload on an FPGA demonstrate that TTmaVTA achieves a 2.86% reduction in execution time, primarily due to improved memory scheduling and conflict resolution; however, resource overhead limits TTmaVTA scalability for larger workloads. Software simulations of OPTTmaVTA with ResNet-18 show a 12.68% improvement in memory access time through prefetching and an 8.75% gain through caching. Overall, the OPTTmaVTA architecture achieves improved memory throughput, with a total latency reduction of approximately 19.86% across all memory operations. In culmination, a scheduling algorithm maps memory access patterns to predefined schedules, ensuring deterministic execution and adherence to real-time constraints. Through a combination of theoretical analysis and practical evaluations, this work makes a substantial contribution to hardware-software co-design for neural network accelerators, particularly suited for applications in safety-critical domains.Source Type:Doctoral Thesis8 6 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Time-Efficient Identification Procedure for Neurological Complications of Rescue Patients in an Emergency Scenario Using Hardware-Accelerated Artificial Intelligence ModelsDuring an emergency rescue operation, rescuers have to deal with many different health complications like cardiovascular, respiratory, neurological, psychiatric, etc. The identification process of the common health complications in rescue events is not very difficult or time-consuming because the health vital symptoms or primary observations are enough to identify, but it is quite difficult with some complications related to neurology e.g., schizophrenia, epilepsy with non-motor seizures, or retrograde amnesia because they cannot be identified with the trend of health vital data. The symptoms have a wide spectrum and are often non-distinguishable from other types of complications. Further, waiting for results from medical tests like MRI and ECG is time-consuming and not suitable for emergency cases where a quick treatment path is an obvious necessity after the diagnosis. In this paper, we present a novel solution for overcoming these challenges by employing artificial intelligence (AI) models in the diagnostic procedure of neurological complications in rescue situations. The novelty lies in the procedure of generating input features from raw rescue data used in AI models, as the data are not like traditional clinical data collected from hospital repositories. Rather, the data were gathered directly from more than 200,000 rescue cases and required natural language processing techniques to extract meaningful information. A step-by-step analysis of developing multiple AI models that can facilitate the fast identification of neurological complications, in general, is presented in this paper. Advanced data analytics are used to analyze the complete record of 273,183 rescue events in a duration of almost 10 years, including rescuers’ analysis of the complications and their diagnostic methods. To develop the detection model, seven different machine learning algorithms-Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbor (KNN), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Naive Bayes (NB) and Artificial Neural Network (ANN) were used. Observing the model’s performance, we conclude that the neural network and extreme gradient boosting show the best performance in terms of selected evaluation criteria. To utilize this result in practical scenarios, the paper also depicts the possibility of embedding such machine learning models in hardware like FPGA. The goal is to achieve fast detection results, which is a primary requirement in any rescue mission. An inference time analysis of the selected ML models and VTA AI accelerator of Apache-TVM machine learning compiler used for the FPGA is also presented in this research.Source Type:Article1
