Hyoukjun Kwon | DeepAI

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∙ 12/07/2022

SDRM3: A Dynamic Scheduler for Dynamic Real-time Multi-model ML Workloads

Emerging real-time multi-model ML (RTMM) workloads such as AR/VR and dro...

0 Seah Kim, et al. ∙

∙ 11/16/2022

XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse

Real-time multi-model multi-task (MMMT) workloads, a new form of deep le...

0 Hyoukjun Kwon, et al. ∙

∙ 06/07/2022

A Formalism of DNN Accelerator Flexibility

The high efficiency of domain-specific hardware accelerators for machine...

0 Sheng-Chun Kao, et al. ∙

∙ 11/01/2021

HRViT: Multi-Scale High-Resolution Vision Transformer

Vision transformers (ViTs) have attracted much attention for their super...

0 Jiaqi Gu, et al. ∙

∙ 06/19/2021

Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication

There is a growing interest in custom spatial accelerators for machine l...

0 Gordon E. Moon, et al. ∙

∙ 03/18/2021

Extending Sparse Tensor Accelerators to Support Multiple Compression Formats

Sparsity, which occurs in both scientific applications and Deep Learning...

0 Eric Qin, et al. ∙

∙ 11/30/2020

Dataflow-Architecture Co-Design for 2.5D DNN Accelerators using Wireless Network-on-Package

Deep neural network (DNN) models continue to grow in size and complexity...

0 Robert Guirado, et al. ∙

∙ 02/18/2020

Marvel: A Data-centric Compiler for DNN Operators on Spatial Accelerators

The efficiency of a spatial DNN accelerator depends heavily on the compi...

0 Prasanth Chatarasi, et al. ∙

∙ 02/18/2020

MARVEL: A Decoupled Model-driven Approach for Efficiently Mapping Convolutions on Spatial DNN Accelerators

The efficiency of a spatial DNN accelerator depends heavily on the compi...

0 Prasanth Chatarasi, et al. ∙

∙ 02/10/2020

Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks

Neural Architecture Search (NAS) has demonstrated its power on various A...

0 Lei Yang, et al. ∙

∙ 12/03/2019

Understanding the Impact of On-chip Communication on DNN Accelerator Performance

Deep Neural Networks have flourished at an unprecedented pace in recent ...

0 Robert Guirado, et al. ∙

∙ 09/13/2019

HERALD: Optimizing Heterogeneous DNN Accelerators for Edge Devices

Recent advances in deep neural networks (DNNs) have made DNNs the backbo...

0 Hyoukjun Kwon, et al. ∙

∙ 05/04/2018

MAESTRO: An Open-source Infrastructure for Modeling Dataflows within Deep Learning Accelerators

We present MAESTRO, a framework to describe and analyze CNN dataflows, a...

0 Hyoukjun Kwon, et al. ∙

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