Animesh Trivedi receives NWO KLEIN-1 grant

In machine learning, large models are associated with more accuracy and intelligence. Building and training of large models processes large amounts of data in CPU-attached and accelerator on-board DRAM memories, which are not scalable, energy-inefficient, and extremely expensive, thus putting large model training out of reach for many users. They propose to leverage Non-Volatile Memory (NVM) storage technology to design a Machine-learning-from-Storage model training paradigm, where data is dynamically moved between NVM storage and DRAM memories on-demand, thus unleashing a new class of previously-unavailable cost- and energy-efficiency for all.

Sixteen innovative research projects launched through Domain Science-KLEIN NWO.