14 citations · 24 across the 6 of their papers we have counts for
9 papers
Towards Enabling Dynamic Convolution Neural Network Inference for Edge Intelligence
Adewale Adeyemo, Travis Sandefur, Tolulope A. Odetola +1
Deep learning applications have achieved great success in numerous real-world applications. Deep learning models, especially Convolution Neural Networks (CNN) are often prototyped…
Security Analysis of Capsule Network Inference using Horizontal Collaboration
Adewale Adeyemo, Faiq Khalid, Tolulope A. Odetola +1
The traditional convolution neural networks (CNN) have several drawbacks like the Picasso effect and the loss of information by the pooling layer. The Capsule network (CapsNet) was…
Dynamic Distribution of Edge Intelligence at the Node Level for Internet of Things
Hawzhin Mohammed, Tolulope A. Odetola, Nan Guo +1
In this paper, dynamic deployment of Convolutional Neural Network (CNN) architecture is proposed utilizing only IoT-level devices. By partitioning and pipelining the CNN, it horizo…
FeSHI: Feature Map Based Stealthy Hardware Intrinsic Attack
Tolulope Odetola, Faiq Khalid, Travis Sandefur +2
To reduce the time-to-market and access to state-of-the-art techniques, CNN hardware mapping and deployment on embedded accelerators are often outsourced to untrusted third parties…
SoWaF: Shuffling of Weights and Feature Maps: A Novel Hardware Intrinsic Attack (HIA) on Convolutional Neural Network (CNN)
Tolulope A. Odetola, Syed Rafay Hasan
Security of inference phase deployment of Convolutional neural network (CNN) into resource constrained embedded systems (e.g. low end FPGAs) is a growing research area. Using secur…
How Secure is Distributed Convolutional Neural Network on IoT Edge Devices?
Hawzhin Mohammed, Tolulope A. Odetola, Syed Rafay Hasan
Convolutional Neural Networks (CNN) has found successful adoption in many applications. The deployment of CNN on resource-constrained edge devices have proved challenging. CNN dist…