In this article, we propose a novel approach to accelerate deep generative AI models, particularly Transformers, using heterogeneous computing architectures. By integrating different processing elements on a single chip, we can improve the computational efficiency and reduce memory bandwidth demands, resulting in faster training times and better performance. This innovation has significant implications for various industries, including finance, healthcare, and natural language processing.
Heterogeneous Architecture
Our proposed architecture consists of multiple computation kernels (❶-❺) that process different parts of the input data in parallel. These kernels are designed to handle different data types and computational requirements, such as matrix multiplication and attention calculations. By exploiting the strengths of each kernel, we can accelerate the training of Transformer models while minimizing memory accesses.
2.5D Heterogeneous Integration
One approach to achieving efficient computation is by integrating multiple processing elements on a single chip. We propose a 2.5D architecture, where different layers are stacked on top of each other but remain separate devices. This design allows for parallelization and reduces the communication overhead between layers, leading to improved performance.
3D Heterogeneous Integration
Another approach is to create a fully integrated 3D structure with multiple processing elements. In this case, each layer is connected to its neighbors through a network of interconnected wires, allowing for faster data transfer and more efficient computation. While this design offers the potential for even greater performance gains, it also poses challenges in terms of complexity and scalability.
Parallelization
To achieve further acceleration, we propose parallelizing the computation across multiple processing elements. By dividing the input data into smaller chunks and processing each chunk simultaneously, we can reduce the training time of Transformer models without sacrificing accuracy. This approach is particularly effective in situations where the input data is large or complex.
Conclusion
In conclusion, our proposed heterogeneous architecture offers a promising solution to accelerate Transformer models while reducing computational requirements and memory bandwidth demands. By integrating multiple processing elements on a single chip or exploiting their strengths through parallelization, we can improve the efficiency and performance of deep generative AI models. As these models continue to play an increasingly important role in various industries, our innovation has the potential to significantly impact fields such as finance, healthcare, and natural language processing.
