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Transformer models gain popularity because of their superior inferenceaccuracy and inference throughput. However, the transformer iscomputation-intensive, causing a long inference time. The existing works ontransformer inference acceleration have limitations caused by either themodification of transformer architectures or the need of specialized hardware.In this paper, we identify the opportunities of using memoization to acceleratethe self-attention mechanism in transformers without the above limitations.Built upon a unique observation that there is rich similarity in attentioncomputation across inference sequences, we build a memoization database thatleverages the emerging big memory system. We introduce a novel embeddingtechnique to find semantically similar inputs to identify computationsimilarity. We also introduce a series of techniques such as memory mapping andselective memoization to avoid memory copy and unnecessary overhead. We enable22% inference-latency reduction on average (up to 68%) with negligible loss ininference accuracy.

AttMEMO : Accelerating Transformers with Memoization on Big Memory Systems