IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs

arXiv:2405.02842v1 Announce Type: new
Abstract: One limitation of existing Transformer-based models is that they cannot handle very long sequences as input since their self-attention operations exhibit quadratic time and space complexity. This problem becomes especially acute when Transformers are deployed on hardware platforms equipped only with CPUs. To address this issue, we propose a novel method for accelerating self-attention at inference time that works with pretrained Transformer models out-of-the-box without requiring retraining. We experiment using our method to accelerate various …

arxiv cpus cs.lg inference transformers type