Transformer (Vaswani et al., 2017) has demon-strated strong performance across a range of nat-ural language processing (NLP) tasks. Recently, learning multiscale Transformer models has …

FT-Transformer is designed to provide resilient and reliable infer-ence against soft errors, which silently corrupt data by bit-flips and lead to incorrect inference results without any visible failure.

Now that we have discussed each operation individually as implemented in the Transformer architecture, Figure 10 depicts the end-to-end flow of the internal operations in the …

Transformer model adoption is further accelerated as specialized hardware is developed by commercial players to improve model training and inference speed. 17 NVIDIA’s Hopper …

In this work, we propose a new eficient construc-tion, Transformer in Transformer (in short, TINT), that allows a transformer to simulate and fine-tune more complex models during inference …

1 Preliminaries Let’s start by talking about the form of the data that is input into a transformer, the goal of the transformer, and the form of its output.

In Section 3, we present a systematic reviewing of Vanilla Transformer, Vision Transformer, and multimodal Transformers, from a geometrically topological perspective.

Here, we continue the line of research on the latter. We claim that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a …

A transformer layer contains a pair of multi-head attention (MHA) and feed-forward network (FFN), and almost all of the prior works focused on finding a combination of them that works best, or …

In summary, we (1) introduce the Adaptive Patch Transformer (APT), which accelerates Vision Transformers by up to 40%through content-aware patch sizes, with larger gains at higher …