Large deep learning models have achieved impressive performance across a range of applications. However, their large memory requirements, including parameter memory and activation memory, have become a significant challenge for their practical serving. While existing methods mainly address parameter memory, the importance of activation memory has been overlooked. Especially for long input sequences, activation memory is expected to experience a significant exponential growth as the length of sequences increases. In this approach, we propose AutoChunk, an automatic and adaptive compiler system that efficiently reduces activation memory for long sequence inference by chunk strategies. The proposed system generates chunk plans by optimizing through multiple stages. In each stage, the chunk search pass explores all possible chunk candidates and the chunk selection pass identifies the optimal one. At runtime, AutoChunk employs code generation to automatically apply chunk strategies. The experiments demonstrate that AutoChunk can reduce over 80% of activation memory while maintaining speed loss within 10%, extend max sequence length by 3.2x to 11.7x, and outperform state-of-the-art methods by a large margin.

While extensive research has focused on optimizing performance and efficiency in vision-based AI accelerators, an unexplored phenomenon, Clustering Similarity Effect, presents a significant opportunity for further improvement. This effect reveals that clusters of neighboring data points exhibit similar values, enabling the potential to skip redundant computations.To fully capitalize on the potential of the Clustering Similarity Effect (CSE), this work integrates hybrid bonding DRAM technology. We conduct a comprehensive analysis of the associated design considerations and integration overhead. Leveraging these insights, we propose a novel CSE-aware architecture specifically tailored for hybrid bonding memory. This architecture facilitates similarity detection and adapts to the inherent data characteristics associated with CSE.Compared with state-of-the-art 2D/2.5D AI accelerators, the hybrid bonding baseline demonstrates an average energy efficiency improvement of $2.89 \times \sim 14.28 \times$ and an area efficiency improvement of $2.67 \times \sim 7.68 \times$. Incorporating the similarity optimizations further enhances energy efficiency and area efficiency improvement to $5.69 \times \sim 28.13 \times$ and $3.82 \times \sim 10.98 \times$, respectively.

Protein structure prediction helps to understand gene translation and protein function, which is of growing interest and importance in structural biology. The AlphaFold model, which used transformer architecture to achieve atomic-level accuracy in protein structure prediction, was a significant breakthrough. However, training and inference of AlphaFold model are challenging due to its high computation and memory cost. In this work, we present FastFold, an efficient implementation of AlphaFold for both training and inference. We propose Dynamic Axial Parallelism (DAP) as a novel model parallelism method. Additionally, we have implemented a series of low-level optimizations aimed at reducing communication, computation, and memory costs. These optimizations include Duality Async Operations, highly optimized kernels, and AutoChunk (an automated search algorithm finds the best chunk strategy to reduce memory peaks). Experimental results show that FastFold can efficiently scale to more GPUs using DAP and reduces overall training time from 11 days to 67 hours and achieves 7.5 ~ 9.5× speedup for long-sequence inference. Furthermore, AutoChunk can reduce memory cost by over 80% during inference by automatically partitioning the intermediate tensors during the computation.