19 citations · 62 across the 13 of their papers we have counts for
14 papers
PipeOrgan: Efficient Inter-operation Pipelining with Flexible Spatial Organization and Interconnects
Raveesh Garg, Hyoukjun Kwon, Eric Qin +3
Because of the recent trends in Deep Neural Networks (DNN) models being memory-bound, inter-operator pipelining for DNN accelerators is emerging as a promising optimization. Inter-…
Characterizing the Accuracy -- Efficiency Trade-off of Low-rank Decomposition in Language Models
Chakshu Moar, Faraz Tahmasebi, Michael Pellauer +1
Recent large language models (LLMs) employ billions of parameters to enable broad problem-solving capabilities. Such language models also tend to be memory-bound because of the dom…
DREAM: A Dynamic Scheduler for Dynamic Real-time Multi-model ML Workloads
Seah Kim, Hyoukjun Kwon, Jinook Song +4
Emerging real-time multi-model ML (RTMM) workloads such as AR/VR and drone control involve dynamic behaviors in various granularity; task, model, and layers within a model. Such dy…
XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse
Hyoukjun Kwon, Krishnakumar Nair, Jamin Seo +15
Real-time multi-task multi-model (MTMM) workloads, a new form of deep learning inference workloads, are emerging for applications areas like extended reality (XR) to support metave…
A Formalism of DNN Accelerator Flexibility
Sheng-Chun Kao, Hyoukjun Kwon, Michael Pellauer +2
The high efficiency of domain-specific hardware accelerators for machine learning (ML) has come from specialization, with the trade-off of less configurability/ flexibility. There…
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation
Jiaqi Gu, Hyoukjun Kwon, Dilin Wang +6
Vision Transformers (ViTs) have emerged with superior performance on computer vision tasks compared to convolutional neural network (CNN)-based models. However, ViTs are mainly des…