1 code implementation • 18 Jul 2023 • Manuel Le Gallo, Corey Lammie, Julian Buechel, Fabio Carta, Omobayode Fagbohungbe, Charles Mackin, Hsinyu Tsai, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui, Malte J. Rasch
In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github. com/IBM/aihwkit.
no code implementations • 15 May 2022 • Omobayode Fagbohungbe, Lijun Qian
The fast execution speed and energy efficiency of analog hardware has made them a strong contender for deployment of deep learning model at the edge.
no code implementations • 8 May 2022 • Omobayode Fagbohungbe, Lijun Qian
However, significant performance degradation suffered by deep learning models due to the inherent noise present in the analog computation can limit their use in mission-critical applications.
no code implementations • 7 May 2022 • Omobayode Fagbohungbe, Lijun Qian
In this work, the use of L1 or TopK BatchNorm type, a fundamental DNN model building block, in designing DNN models with excellent noise-resistant property is proposed.
no code implementations • 19 Apr 2021 • Bo Yang, Omobayode Fagbohungbe, Xuelin Cao, Chau Yuen, Lijun Qian, Dusit Niyato, Yan Zhang
In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic.
no code implementations • 24 Nov 2020 • Omobayode Fagbohungbe, Lijun Qian
Analog hardware implemented deep learning models are promising for computation and energy constrained systems such as edge computing devices.
no code implementations • 10 May 2020 • Omobayode Fagbohungbe, Sheikh Rufsan Reza, Xishuang Dong, Lijun Qian
In order to extract knowledge from the large data collected by edge devices, traditional cloud based approach that requires data upload may not be feasible due to communication bandwidth limitation as well as privacy and security concerns of end users.