Efficient Methods for Natural Language Processing: A Survey

no code implementations • 31 Aug 2022 • Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon Derczynski, Iryna Gurevych, Roy Schwartz

Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows.

Information Retrieval Open-Domain Question Answering

On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers

no code implementations • Findings (ACL) 2021 • Tianchu Ji, Shraddhan Jain, Michael Ferdman, Peter Milder, H. Andrew Schwartz, Niranjan Balasubramanian

This informs the design of an inference-time quantization technique using both pruning and log-scaled mapping which produces only a few (e. g. $2^3$) unique values.

Quantization Question Answering +1

Medusa: A Scalable Interconnect for Many-Port DNN Accelerators and Wide DRAM Controller Interfaces

no code implementations • 11 Jul 2018 • Yongming Shen, Tianchu Ji, Michael Ferdman, Peter Milder

To cope with the increasing demand and computational intensity of deep neural networks (DNNs), industry and academia have turned to accelerator technologies.