30 citations · 39 across the 4 of their papers we have counts for
4 papers
FlexBlock: A Flexible DNN Training Accelerator with Multi-Mode Block Floating Point Support
Seock-Hwan Noh, Jahyun Koo, Seunghyun Lee +2
Training deep neural networks (DNNs) is a computationally expensive job, which can take weeks or months even with high performance GPUs. As a remedy for this challenge, community h…
Multi-model Machine Learning Inference Serving with GPU Spatial Partitioning
Seungbeom Choi, Sunho Lee, Yeonjae Kim +3
As machine learning techniques are applied to a widening range of applications, high throughput machine learning (ML) inference servers have become critical for online service appl…
Stockade: Hardware Hardening for Distributed Trusted Sandboxes
Joongun Park, Seunghyo Kang, Sanghyeon Lee +4
The widening availability of hardware-based trusted execution environments (TEEs) has been accelerating the adaptation of new applications using TEEs. Recent studies showed that a…
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks
Hardik Sharma, Jongse Park, Naveen Suda +5
Fully realizing the potential of acceleration for Deep Neural Networks (DNNs) requires understanding and leveraging algorithmic properties. This paper builds upon the algorithmic i…