activity
20192026
most citedNeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

OpenAttention: One Percent Is Enough with Tail Proxies

Hyunwoo Yu, Jongbeom Lee, Hyunbin Kim +8

Sparse attention reads only a small subset of the key-value cache, and how to treat the unread tail remains a key challenge. Discarding the tail biases the output, and sampling it…

cs.AR2025

PIM-malloc: A Fast and Scalable Dynamic Memory Allocator for Processing-In-Memory (PIM) Architectures

Dongjae Lee, Bongjoon Hyun, Youngjin Kwon +1

The ability to dynamically allocate memory is fundamental in modern programming languages. However, this feature is not adequately supported in current general-purpose PIM devices.…

cs.AR2024

PIM-MMU: A Memory Management Unit for Accelerating Data Transfers in Commercial PIM Systems

Dongjae Lee, Bongjoon Hyun, Taehun Kim +1

Processing-in-memory (PIM) has emerged as a promising solution for accelerating memory-intensive workloads as they provide high memory bandwidth to the processing units. This appro…

cs.AR2023

Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology

Bongjoon Hyun, Taehun Kim, Dongjae Lee +1

Processing-in-memory (PIM) has been explored for decades by computer architects, yet it has never seen the light of day in real-world products due to their high design overheads an…

cs.AR2019★ 1 cited

NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units

Bongjoon Hyun, Youngeun Kwon, Yujeong Choi +2

To satisfy the compute and memory demands of deep neural networks, neural processing units (NPUs) are widely being utilized for accelerating deep learning algorithms. Similar to ho…