Software Engineers Can Now Build Efficient Transformers
Gated Linear Attention Transformers (GLAT) improve efficiency & performance on resource-constrained devices like smartphones & IoT sensors with a linear-complexity attention mechanism & hardware-aware training.
This is a Plain English Papers summary of a research paper called Gated Linear Attention Transformers with Hardware-Efficient Training. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter. Overview This paper introduces a new type of attention mechanism called Gated Linear Attention Transformers (GLAT), which aims to improve the efficiency of transformers for hardware-constrained applications. The key innovations include a gated linear attention mechanism that reduces the computational complexity of attention, and a hardwar...