124 citations · 311 across the 19 of their papers we have counts for
4 papers · 1 filter
Training Neural Machine Translation (NMT) Models using Tensor Train Decomposition on TensorFlow (T3F)
Amelia Drew, Alexander Heinecke
We implement a Tensor Train layer in the TensorFlow Neural Machine Translation (NMT) model using the t3f library. We perform training runs on the IWSLT English-Vietnamese '15 and W…
High-Performance Deep Learning via a Single Building Block
Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar +6
Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL librari…
A Study of BFLOAT16 for Deep Learning Training
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi +16
This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training acros…
Leveraging the bfloat16 Artificial Intelligence Datatype For Higher-Precision Computations
Greg Henry, Ping Tak Peter Tang, Alexander Heinecke
In recent years fused-multiply-add (FMA) units with lower-precision multiplications and higher-precision accumulation have proven useful in machine learning/artificial intelligence…