The report points out that the IT technology stack in the era of artificial intelligence has undergone significant changes compared to the era of PC and mobile internet. The traditional three-layer architecture of chips, operating systems, and applications has evolved into a four-layer system of chips, frameworks, models, and applications. The new architecture not only improves the collaboration between layers, but also promotes the continuous optimization of the system, providing key support for the rapid iteration of generative AI.
In the ecosystem of generative AI models, the chip layer, framework layer, model layer, and application layer each play a key role in jointly driving the development and application of generative AI technology.
Chip layer
Provide powerful computing power: Training generative AI models requires extremely high computing resources, especially for processing large-scale data and complex neural network structures. Efficient computing power at the chip layer is the foundation to support these tasks. For example, specialized chips such as GPUs (graphics processing units) and TPUs (tensor processing units) can quickly perform a large number of matrix operations through parallel processing capabilities, significantly accelerating the training and inference process of models.
Optimize energy efficiency: As the scale of the model increases, the issue of energy consumption becomes more and more prominent. The chip layer needs to be continuously optimized to improve computing efficiency and reduce energy consumption. For example, by improving chip architectures, employing more advanced manufacturing processes, and developing specialized compute-optimized technologies, chips are able to reduce energy consumption while maintaining high performance.
Frame layer
Simplify model development: Deep learning frameworks (e.g., TensorFlow, PyTorch) provide a rich set of tools and libraries that make it easier for researchers and developers to build, train, and deploy generative AI models. These frameworks encapsulate many of the low-level details, such as automatic differentiation, tensor manipulation, etc., allowing developers to focus on model design and algorithm implementation.
Provides scalability and flexibility: The deep learning framework supports a variety of model architectures and algorithms, making it highly scalable. Developers can select the appropriate model structure and algorithm according to their needs for customized development. At the same time, the framework layer also provides a wealth of interfaces and plug-in mechanisms to facilitate developers to extend functions and integrate external tools.
Figure: The IT tech stack in the age of artificial intelligence has undergone significant changes
Model layers
Define model architecture and algorithms: The model layer is the core of generative AI, which defines the architecture of the model (such as Transformer architecture, Diffusion Models, etc.) and training algorithms (such as pre-training + fine-tuning, reinforcement learning, etc.). Different model architectures and algorithms determine the performance, efficiency, and application scope of the model. For example, the self-attention mechanism of the Transformer architecture allows the model to efficiently handle long-distance dependencies, while Diffusion Models generates high-quality images through stepwise denoising.
Learn representations and patterns of data: The model layer learns representations and patterns in the data by training, enabling it to generate new content that is similar to the input data. During training, the model continuously adjusts parameters to minimize the discrepancy between the predicted and real-world data.
Support for multimodal learning and fusion: Modern generative models are capable of processing and fusing multimodal data beyond just a single modality of data, such as text or images. For example, by co-modeling text and image data, a model can generate illustrated content, or an image based on a text description.
Application layer
Meet the needs of different scenarios: The application layer applies generative AI models to various real-world scenarios, such as natural language generation, content creation, intelligent customer service, education, etc. In these scenarios, the model is able to generate useful content based on specific needs.
Improve interactivity and user experience: The application layer makes generative AI models easily accessible to ordinary users by designing user-friendly user interfaces and interaction mechanisms.
Promote innovation and business value: The development and promotion of the application layer promotes the innovation and realization of business value of generative AI technologies. By applying the model to different domains and scenarios, new application scenarios and business models can be discovered. For example, some companies have begun to leverage generative models for product design, marketing, and customer service, with good results.
This hierarchical structure not only makes generative AI models more technically efficient and powerful, but also promotes their wide application in various fields, bringing great opportunities for social and economic development.
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