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Found 7 papers, 1 papers with code
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
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.
Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models
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.
Impact of Learning Rate on Noise Resistant Property of Deep Learning Models
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.
Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models
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.
A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks
In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic.
Benchmarking Inference Performance of Deep Learning Models on Analog Devices
Analog hardware implemented deep learning models are promising for computation and energy constrained systems such as edge computing devices.
Efficient Privacy Preserving Edge Computing Framework for Image Classification
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.
