SpARC: Token Similarity-Aware Sparse Attention Transformer Accelerator via Row-wise Clustering

Self-attention mechanisms, the key enabler of transformers' remarkable performance, account for a significant portion of the overall transformer computation. Despite its effectiveness, self-attention inherently contains considerable redundancies, making sparse attention an attractive approach. In this paper, we propose SpARC, a sparse attention transformer accelerator that enhances throughput and energy efficiency by reducing the computational complexity of the self-attention mechanism. Our approach exploits inherent row-level redundancies in transformer attention maps to reduce the overall self-attention computation. By employing row-wise clustering, attention scores are calculated only once per cluster to achieve approximate attention without seriously compromising accuracy. To leverage the high parallelism of the proposed clustering approximate attention, we develop a fully pipelined accelerator with a dedicated memory hierarchy. Experimental results demonstrate that SpARC achieves attention map sparsity levels of 85-90% with negligible accuracy loss. SpARC achieves up to 4× core attention speedup and 6× energy efficiency improvement compared to prior sparse attention transformer accelerators.