**Myth:** Neural Networks Only Thrive on High-Computational

Myth: Neural Networks Only Thrive on High-Computational Power

Reality: While true that high-performance computing is necessary for large-scale neural network models, the field has seen a significant shift in the past few years towards more efficient and scalable neural network designs.

With the rise of Model-Parallel Training (MPT) and knowledge distillation techniques, researchers have been able to reduce complexity in neural network architectures and still attain state-of-the-art results with lower computational requirements. Furthermore, breakthroughs in sparse neural networks, graph neural networks, and even neural networks with non-linear activation functions tailored for low-power devices can achieve performance close to traditional neural networks using significantly lower power consumption. The future of neural networks is not just about powerful hardware, but also optimized software.

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