LAPA: Log-Domain Prediction-Driven Dynamic Sparsity Accelerator for Transformer Model
This work addresses energy efficiency issues in deploying Transformer models for NLP and CV tasks, representing an incremental hardware optimization.
The paper tackles the dynamic computational bottlenecks in Transformer models by proposing LAPA, a log-domain prediction-driven dynamic sparsity accelerator, which achieves 3.52x, 3.24x, and 2.79x higher energy efficiency compared to state-of-the-art works.
Attention-based Transformers have revolutionized natural language processing (NLP) and shown strong performance in computer vision (CV) tasks. However, as the input sequence varies, the computational bottlenecks in Transformer models exhibit dynamic behavior across stages, which calls for a cross-stage sparse acceleration strategy. Unfortunately, most existing sparse Transformer approaches are single-stage based, and their sparsity prediction mechanisms lead to significant power overhead when applied across multiple stages. To this end, this paper proposes a log-domain attention prediction algorithm-architecture co-design, named LAPA. First, an asymmetric leading one computing (ALOC) scheme is designed to eliminate expensive multiplications. Next, a mixed-precision multi-round shifting accumulation (MRSA) mechanism is further proposed to mitigate the accumulation overhead. A data-feature dependent filter (DDF) strategy is designed to work in concert with the MRSA process. Finally, an elaborate accelerator is designed to translate the theoretical enhancement into practical hardware improvement. Experimental results show that LAPA achieves 3.52x, 3.24x and 2.79x higher energy efficiency than the state-of-the-art (SOTA) works Spatten, Sanger and FACT, respectively.