1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2024
MoNDE: Mixture of Near-Data Experts for Large-Scale Sparse Models
Taehyun Kim, Kwanseok Choi, Youngmock Cho +3
Mixture-of-Experts (MoE) large language models (LLM) have memory requirements that often exceed the GPU memory capacity, requiring costly parameter movement from secondary memories…
cs.LG2021★ 1 cited
GradPIM: A Practical Processing-in-DRAM Architecture for Gradient Descent
Heesu Kim, Hanmin Park, Taehyun Kim +6
In this paper, we present GradPIM, a processing-in-memory architecture which accelerates parameter updates of deep neural networks training. As one of processing-in-memory techniqu…