Jun 15, 2020 · This paper introduces the concept of Neural Weight Virtualization - which enables fast and scalable in-memory multitask deep learning on memory-constrained ...
Neural weight virtualization is implemented by using Python, TensorFlow, and NVIDIA CUDA (custom TensorFlow operation). The TensorFlow version should be lower ...
These DNNs, each trained for a specific inference task, are often run simultaneously on resource-constrained robotic platforms.
Duration: 17:35
Posted: Jun 15, 2020
Posted: Jun 15, 2020
Fast and scalable in-memory deep multitask learning via neural weight virtualization. S Lee, S Nirjon. Proceedings of the 18th International Conference on ...
[MobiSys 2020] "Fast and Scalable In-memory Deep Multitask Learning via Neural Weight Virtualization". The 18th ACM International Conference on Mobile ...
IMWUT/UbiComp 2020. Fast and Scalable In-memory Deep Multitask Learning via Neural Weight Virtualization Seulki Lee and Shahriar Nirjon MobiSys 2020. Wi ...
Oct 2, 2023 · This document introduces neural weight virtualization, which enables fast and scalable in-memory multitask deep learning on ...
Jun 17, 2020 · MobiSys 2020 - Fast and Scalable In-memory Deep Multitask Learning via Neural Weight Virtualization. ACM SIGMOBILE ONLINE · 17:27 · MobiSys2020 ...
Duration: 17:35
Posted: Jun 7, 2021
Posted: Jun 7, 2021