66 citations · 112 across the 9 of their papers we have counts for
12 papers
Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters
Zhaoxian Wu, Tayfun Gokmen, Omobayode Fagbohungbe +2
Analog in-memory computing (AIMC) offers an alternative for model training by executing matrix operations directly where weights are stored. However, scaling AIMC to train modern d…
Assessing the Performance of Analog Training for Transfer Learning
Omobayode Fagbohungbe, Corey Lammie, Malte J. Rasch +3
Analog in-memory computing is a next-generation computing paradigm that promises fast, parallel, and energy-efficient deep learning training and transfer learning (TL). However, ac…
Analog Foundation Models
Julian Büchel, Iason Chalas, Giovanni Acampa +7
Analog in-memory computing (AIMC) is a promising compute paradigm to improve speed and power efficiency of neural network inference beyond the limits of conventional von Neumann-ba…
Analog In-memory Training on General Non-ideal Resistive Elements: The Impact of Response Functions
Zhaoxian Wu, Quan Xiao, Tayfun Gokmen +2
As the economic and environmental costs of training and deploying large vision or language models increase dramatically, analog in-memory computing (AIMC) emerges as a promising en…
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
Manuel Le Gallo, Corey Lammie, Julian Buechel +8
Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and n…
Fast offset corrected in-memory training
Malte J. Rasch, Fabio Carta, Omebayode Fagbohungbe +1
In-memory computing with resistive crossbar arrays has been suggested to accelerate deep-learning workloads in highly efficient manner. To unleash the full potential of in-memory c…
Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models
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. However, there are concerns ab…
Impact of Learning Rate on Noise Resistant Property of Deep Learning Models
Omobayode Fagbohungbe, Lijun Qian
The interest in analog computation has grown tremendously in recent years due to its fast computation speed and excellent energy efficiency, which is very important for edge and Io…
Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models
Omobayode Fagbohungbe, Lijun Qian
Analog hardware has become a popular choice for machine learning on resource-constrained devices recently due to its fast execution and energy efficiency. However, the inherent pre…
A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks
Bo Yang, Omobayode Fagbohungbe, Xuelin Cao +4
In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic. In particular, the edge server…
Benchmarking Inference Performance of Deep Learning Models on Analog Devices
Omobayode Fagbohungbe, Lijun Qian
Analog hardware implemented deep learning models are promising for computation and energy constrained systems such as edge computing devices. However, the analog nature of the devi…
Efficient Privacy Preserving Edge Computing Framework for Image Classification
Omobayode Fagbohungbe, Sheikh Rufsan Reza, Xishuang Dong +1
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 ban…