👉 Neural network engineering (NNE) is an advanced approach to designing, optimizing, and deploying neural networks, focusing on creating more efficient, scalable, and adaptable models. It integrates principles from computer engineering, electrical engineering, and machine learning to address challenges such as computational efficiency, energy consumption, and hardware compatibility. NNE emphasizes the development of novel architectures, training algorithms, and integration strategies that leverage specialized hardware (like GPUs, TPUs) and software frameworks to enhance performance, reduce latency, and enable real-time processing. By bridging the gap between theoretical neural network design and practical implementation, NNE aims to accelerate innovation in AI while making models more accessible and sustainable for diverse applications, from edge devices to large-scale data centers.
