56 citations · 58 across the 5 of their papers we have counts for
5 papers
PIMphony: Overcoming Bandwidth and Capacity Inefficiency in PIM-based Long-Context LLM Inference System
Hyucksung Kwon, Kyungmo Koo, Janghyeon Kim +19
The expansion of long-context Large Language Models (LLMs) creates significant memory system challenges. While Processing-in-Memory (PIM) is a promising accelerator, we identify th…
IANUS: Integrated Accelerator based on NPU-PIM Unified Memory System
Minseok Seo, Xuan Truong Nguyen, Seok Joong Hwang +18
Accelerating end-to-end inference of transformer-based large language models (LLMs) is a critical component of AI services in datacenters. However, diverse compute characteristics…
Darwin: A DRAM-based Multi-level Processing-in-Memory Architecture for Data Analytics
Donghyuk Kim, Jae-Young Kim, Wontak Han +4
Processing-in-memory (PIM) architecture is an inherent match for data analytics application, but we observe major challenges to address when accelerating it using PIM. In this pape…
Near Data Acceleration with Concurrent Host Access
Benjamin Y. Cho, Yongkee Kwon, Sangkug Lym +1
Near-data accelerators (NDAs) that are integrated with main memory have the potential for significant power and performance benefits. Fully realizing these benefits requires the la…
Mini-batch Serialization: CNN Training with Inter-layer Data Reuse
Sangkug Lym, Armand Behroozi, Wei Wen +3
Training convolutional neural networks (CNNs) requires intense computations and high memory bandwidth. We find that bandwidth today is over-provisioned because most memory accesses…