11 citations · 24 across the 5 of their papers we have counts for
4 papers
A Data-Centric Approach for Training Deep Neural Networks with Less Data
Mohammad Motamedi, Nikolay Sakharnykh, Tim Kaldewey
While the availability of large datasets is perceived to be a key requirement for training deep neural networks, it is possible to train such models with relatively little data. Ho…
Resource-Scalable CNN Synthesis for IoT Applications
Mohammad Motamedi, Felix Portillo, Mahya Saffarpour +2
State-of-the-art image recognition systems use sophisticated Convolutional Neural Networks (CNNs) that are designed and trained to identify numerous object classes. Such networks a…
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms
Mohammad Motamedi, Felix Portillo, Daniel Fong +1
Many Internet-of-Things (IoT) applications demand fast and accurate understanding of a few key events in their surrounding environment. Deep Convolutional Neural Networks (CNNs) ha…
Cappuccino: Efficient Inference Software Synthesis for Mobile System-on-Chips
Mohammad Motamedi, Daniel Fong, Soheil Ghiasi
Convolutional Neural Networks (CNNs) exhibit remarkable performance in various machine learning tasks. As sensor-equipped Internet of Things (IoT) devices permeate into every aspec…