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- research-articleSeptember 2023
DaCapo: An On-Device Learning Scheme for Memory-Constrained Embedded Systems
ACM Transactions on Embedded Computing Systems (TECS), Volume 22, Issue 5sArticle No.: 142, Pages 1–23https://doi.org/10.1145/3609121The use of deep neural network (DNN) applications in microcontroller unit (MCU) embedded systems is getting popular. However, the DNN models in such systems frequently suffer from accuracy loss due to the dataset shift problem. On-device learning resolves ...
- ArticleNovember 2021
Robustness Analysis of Deep Learning Frameworks on Mobile Platforms
AbstractWith the recent increase in the computational power of modern mobile devices, machine learning-based heavy tasks such as face detection and speech recognition are now integral parts of such devices. This requires frameworks to execute machine ...
- ArticleAugust 2020
- research-articleJuly 2019
Architectures and algorithms for on-device user customization of CNNs
Integration, the VLSI Journal (INTG), Volume 67, Issue CPages 121–133https://doi.org/10.1016/j.vlsi.2018.11.001AbstractA convolutional neural network (CNN) architecture supporting on-device user customization is proposed. The network architecture consists of a large CNN trained on a general data and a smaller augmenting network that can be re-trained ...
Highlights- A novel CNN-based network architecture is proposed to enable user specialization.
- research-articleNovember 2018
Semi-Trained Memristive Crossbar Computing Engine with In Situ Learning Accelerator
ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 14, Issue 4Article No.: 43, Pages 1–16https://doi.org/10.1145/3233987On-device intelligence is gaining significant attention recently as it offers local data processing and low power consumption. In this research, an on-device training circuitry for threshold-current memristors integrated in a crossbar structure is ...