TY - GEN
T1 - KnowledgeNet
T2 - 2019 USENIX Conference on Operational Machine Learning, OpML 2019
AU - Biookaghazadeh, Saman
AU - Chen, Yitao
AU - Zhao, Kaiqi
AU - Zhao, Ming
PY - 2019
Y1 - 2019
N2 - Deep Neural Networks (DNNs) have a significant impact on numerous applications, such as video processing, virtual/augmented reality, and text processing. The ever-changing environment forces the DNN models to evolve, accordingly. Also, the transition from the cloud-only to edge-cloud paradigm has made the deployment and training of these models challenging. Addressing these challenges requires new methods and systems for continuous training and distribution of these models in a heterogeneous environment. In this paper, we propose KnowledgeNet (KN), which is a new architectural technique for a simple disaggregation and distribution of the neural networks for both training and serving. Using KN, DNNs can be partitioned into multiple small blocks and be deployed on a distributed set of computational nodes. Also, KN utilizes the knowledge transfer technique to provide small scale models with high accuracy in edge scenarios with limited resources. Preliminary results show that our new method can ensure a state-of-the-art accuracy for a DNN model while being disaggregated among multiple workers. Also, by using knowledge transfer technique, we can compress the model by 62% for deployment, while maintaining the same accuracy.
AB - Deep Neural Networks (DNNs) have a significant impact on numerous applications, such as video processing, virtual/augmented reality, and text processing. The ever-changing environment forces the DNN models to evolve, accordingly. Also, the transition from the cloud-only to edge-cloud paradigm has made the deployment and training of these models challenging. Addressing these challenges requires new methods and systems for continuous training and distribution of these models in a heterogeneous environment. In this paper, we propose KnowledgeNet (KN), which is a new architectural technique for a simple disaggregation and distribution of the neural networks for both training and serving. Using KN, DNNs can be partitioned into multiple small blocks and be deployed on a distributed set of computational nodes. Also, KN utilizes the knowledge transfer technique to provide small scale models with high accuracy in edge scenarios with limited resources. Preliminary results show that our new method can ensure a state-of-the-art accuracy for a DNN model while being disaggregated among multiple workers. Also, by using knowledge transfer technique, we can compress the model by 62% for deployment, while maintaining the same accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85077010688&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077010688&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85077010688
T3 - Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019
SP - 47
EP - 49
BT - Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019
PB - USENIX Association
Y2 - 20 May 2019
ER -