MEGA.mini: A Universal Generative AI Processor with a New Big/Little Core Architecture for NPU

The global AI market is growing explosively with the rise of generative AI applications, such as image manipulation and text-to-text/image/video creation. AI was primarily expected to automate only simple tasks like classification and data analysis. However, the advent of generative AI has transformed it into a creativity assistant, helping people think more creatively by offering new perspectives through deep neural networks (DNNs). As shown in Fig. 23.6.1, there are various types of DNNs used in generative AI, which can be categorized into two groups: non-diffusion models (NDMs) and diffusion models (DMs). NDMs, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers, are still predominantly used. DMs require 25–100x more operations due to their need for iterative inference (INF). Consequently, generative AI processors need to efficiently accelerate both NDMs and DMs.