19 citations · 27 across the 9 of their papers we have counts for
6 papers · 1 filter
COX: CUDA on X86 by Exposing Warp-Level Functions to CPUs
Ruobing Han, Jaewon Lee, Jaewoong Sim +1
As CUDA programs become the de facto program among data parallel applications such as high-performance computing or machine learning applications, running CUDA on other platforms h…
Local Texture Estimator for Implicit Representation Function
Jaewon Lee, Kyong Hwan Jin
Recent works with an implicit neural function shed light on representing images in arbitrary resolution. However, a standalone multi-layer perceptron shows limited performance in l…
Point Cloud Augmentation with Weighted Local Transformations
Sihyeon Kim, Sanghyeok Lee, Dasol Hwang +3
Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation is a standard approach…
Supporting CUDA for an extended RISC-V GPU architecture
Ruobing Han, Blaise Tine, Jaewon Lee +2
With the rapid development of scientific computation, more and more researchers and developers are committed to implementing various workloads/operations on different devices. Amon…
First-Generation Inference Accelerator Deployment at Facebook
Michael Anderson, Benny Chen, Stephen Chen +112
In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesse…
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity
Seo Taek Kong, Soomin Jeon, Dongbin Na +3
Deep learning (DL) algorithms rely on massive amounts of labeled data. Semi-supervised learning (SSL) and active learning (AL) aim to reduce this label complexity by leveraging unl…