Year: 2025
Type: conference-paper
Abstract: Deep neural networks (DNNs) are one of the popular models for learning relationships between complex data. Training a DNN model is a compute- and memory-intensive operation. The size of modern DNN models spans into the terabyte region, requiring multiple accelerators to train -- driving up the training cost. Such humongous memory requirements shift the focus toward memory rather than computation.
Authors: Sathvik Swaminathan, Sandeep Kumar, Aravinda Prasad, Sreenivas Subramoney
Institution: Intel (India)
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411259335