Jose Manuel Bernabe' Murcia, Eduardo Canovas Martinez, et al.
MobiSec 2024
We introduce AIHWKIT-Lightning, a new toolkit designed for efficient and scalable hardware-aware training of large neural networks deployed on Analog In-Memory Computing (AIMC)-based hardware. The toolkit prioritizes speed and ease of use, addressing the limitations of existing frameworks in training Large Language Models (LLMs) with billions of parameters. AIHWKIT-Lightning leverages dedicated GPU kernels and a streamlined implementation, achieving up to 3.7x faster training at lower memory consumption compared to state-of-the-art toolkits. Benefiting from the increased scalability, we demonstrate near-iso-accuracy on the GLUE benchmark using a RoBERTa model trained on 11B tokens. The toolkit is publicly available at github.com/IBM/aihwkit-lightning.
Jose Manuel Bernabe' Murcia, Eduardo Canovas Martinez, et al.
MobiSec 2024
Kristjan Greenewald, Yuancheng Yu, et al.
NeurIPS 2024
Paula Olaya, Sophia Wen, et al.
Big Data 2024
Clara Higuera Cabañes, Ryo Iwaki, et al.
NeurIPS 2024