180 citations · 520 across the 35 of their papers we have counts for
4 papers · 1 filter
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…
PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units
Yujeong Choi, Minsoo Rhu
To amortize cost, cloud vendors providing DNN acceleration as a service to end-users employ consolidation and virtualization to share the underlying resources among multiple DNN se…
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning
Youngeun Kwon, Yunjae Lee, Minsoo Rhu
Recent studies from several hyperscalars pinpoint to embedding layers as the most memory-intensive deep learning (DL) algorithm being deployed in today's datacenters. This paper ad…
Beyond the Memory Wall: A Case for Memory-centric HPC System for Deep Learning
Youngeun Kwon, Minsoo Rhu
As the models and the datasets to train deep learning (DL) models scale, system architects are faced with new challenges, one of which is the memory capacity bottleneck, where the…