A Heterogeneous Chiplet Architecture for Accelerating End-to-End Transformer Models

Understand

Transformers have revolutionized deep learning and generative modeling, enabling advancements in natural language processing tasks.

• However, the size of transformer models is increasing continuously, driven by enhanced capabilities across various deep learning tasks.
• This trend of ever-increasing model size has given rise to new challenges in terms of memory and compute requirements.
• Conventional computing platforms, including GPUs, suffer from suboptimal performance due to the memory demands imposed by models with millions/billions of parameters.