Distillation is a valuable technique that enables the transfer of knowledge from a large and complex neural network (the teacher) to a smaller and simpler neural network (the student). This technique offers a significant reduction in computational cost and energy consumption of a neural network, up to 100 times, while maintaining or improving its performance. Distillation has been successfully applied to numerous domains and tasks, such as natural language processing, computer vision, and speech synthesis, making it a highly promising method for making neural networks more accessible and efficient for various applications. According to Gartner, by the end of 2023, the value of AI-enabled application processors used in devices will amount to $1.2 billion, up from $558 million in 2022. [1] With the power of distillation, AI applications can reach new heights of efficiency and performance even on devices with limited computational resources.
Neural networks are a type of artificial intelligence that has revolutionized various fields such as computer vision, natural language processing, and speech recognition. However, they do come with their own set of limitations. One such drawback is their high computational cost and energy consumption, which can become even more pronounced when they are large and deep. The high computational cost and energy consumption of such large-scale neural networks are a significant challenge to their widespread adoption.
An increasing number of the machine learning (ML) models they build at Apple each year are either partly or fully adopting the Transformer architecture. There is an immense need for on-device inference optimizations to translate these research gains into practice. Optimizations with the the Transformer architecture will enable ML practitioners to deploy much larger models on the same input set, or to deploy the same models to run on much larger sets of inputs within the same compute budget. Applying optimizations resulted in a forward pass that is up to 10 times faster with a simultaneous reduction of peak-memory consumption of 14 times on iPhone 13. Using their reference implementation, on a sequence length of 128 and a batch size of 1, the iPhone 13 ANE achieves an average latency of 3.47 ms at 0.454 W and 9.44 ms at 0.072 W. Even using their reference implementation, the ANE peak throughput was far from being saturated for this particular model configuration, and the performance may be further improved. The 16-core Neural Engine on the A15 Bionic chip on iPhone 13 Pro has a peak throughput of 15.8 teraflops, an increase of 26 times that of iPhone X.[2]
With distillation, you can transfer the knowledge from a large neural network to a smaller one, which can perform the same task with fewer resources. This can lead to faster and better results, ultimately leading to enhanced customer satisfaction and loyalty. A smaller neural network can run more efficiently and faster on various devices like smartphones and IoT devices, providing customers with more responsive and reliable services. Moreover, a smaller neural network can achieve better performance than a larger one or a similarly sized one trained from scratch. It can leverage the knowledge and generalization ability of the larger one, leading to better results. Distillation can also help you enhance your social responsibility and reputation by reducing your environmental impact. A smaller neural network consumes less energy, reducing your carbon footprint and greenhouse gas emissions. By training and running a smaller neural network using distillation, you can produce similar or better results with much less energy, making a positive impact on the environment.
The field of AI is continuously growing, and market leaders are staying ahead by developing novel techniques to improve the accuracy of neural networks. By leveraging the differences among models trained from various randomizations, they are reducing computational costs and memory requirements. This not only helps in training and running the models but also enables them to run on devices with limited battery life, such as smartphones or IoT devices. The technique also enhances the robustness of the models against noise, adversarial attacks, or domain shifts, which is crucial in real-world applications. By learning from the soft probabilities or logits of the teacher network, the models become more stable and smooth, improving their overall performance. Distillation plays an important role in AI in the context of sustainable technology for a business owner, as it can help you save money and resources, improve customer satisfaction and loyalty, and reduce environmental impact. Distillation is a promising method for making neural networks more accessible and efficient for various applications.
