(untitled) (Computer systems and information technologies, 2024)
The effectiveness of convolutional neural networks (CNNs) has been demonstrated across various fields, including computer vision, natural language processing, medical imaging, and autonomous systems. However, achieving high performance in CNNs is not only a matter of model design but also of optimizing the training and inference processes. Using accelerators like the Google Coral TPU provides significant improvements in both computational efficiency and overall model performance. This paper focuses on the integration of the Coral TPU to enhance CNN performance by speeding up computations, reducing latency, and enabling real-time deployment. Training deep learning models, particularly CNNs, is computationally intensive. Traditional CPUs or GPUs can take hours or even days to train large networks on complex data. The accelerator offloads these intensive tasks, allowing the host machine to focus on other operations and making training more efficient. This enables researchers to experiment with multiple architectures and hyperparameters within shorter cycles, thereby improving the model’s accuracy and robustness. CNNs are widely deployed in edge computing scenarios where real-time predictions are critical, such as in robotics, autonomous vehicles, and smart surveillance systems.Unlike traditional cloud-based solutions, where models are executed remotely and suffer from network delays, the Coral TPU ensures low-latency predictions directly on the device, making it ideal for time-sensitive applications. Another key advantage of using accelerators like Coral TPU is the ability to efficiently handle optimized and lightweight models. These optimized models are well-suited for the Coral TPU’s architecture, allowing developers to deploy high-performing networks even on resource-constrained devices. The TPU’s ability to handle quantized models with minimal loss in accuracy further enhances the CNN’s practical usability across various domains. The Coral TPU is designed to minimize power consumption, making it an ideal solution for battery-powered or energy-constrained devices. This energy efficiency ensures that CNNs can run continuously on devices like drones, IoT sensors, or mobile platforms without exhausting their power supply. Additionally, the scalability of the TPU makes it easy to deploy multiple accelerators in parallel, further improving throughput for applications that require processing high volumes of data, such as real-time video analysis. The Coral TPU also facilitates on-device learning, where models can be incrementally updated based on new data without requiring a full retraining session. This feature is particularly useful in dynamic environments, such as autonomous vehicles or security systems, where the model needs to adapt quickly to new conditions. With the TPU handling the computational workload, CNNs can be fine-tuned on the device, ensuring they remain accurate and responsive over time.