2 papers
cs.CV2023
Input Compression with Positional Consistency for Efficient Training and Inference of Transformer Neural Networks
Amrit Nagarajan, Anand Raghunathan
Transformers have rapidly increased in popularity in recent years, achieving state-of-the-art performance in processing text, images, audio and video. However, Transformers present…
cs.CL2020
AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models
Amrit Nagarajan, Sanchari Sen, Jacob R. Stevens +1
Transformers have greatly advanced the state-of-the-art in Natural Language Processing (NLP) in recent years, but present very large computation and storage requirements. We observ…