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Full-Text Articles in Computer Engineering
Mapping Arbitrary Spiking Neural Networks To The Ravens Neuroprocessor, Jongheon Park
Mapping Arbitrary Spiking Neural Networks To The Ravens Neuroprocessor, Jongheon Park
Masters Theses
In neuromorphic computing, a hardware implementation of a spiking neural network is used to provide improved speed and power efficiency over simulations of the networks on a traditional Von Neumann architecture. These hardware implementations employ bio-inspired architecture usually consisting of artificial neurons and synapses implemented in either analog, digital, or mixed-signal circuits. Since these hardware spiking neural networks are designed to support arbitrary networks under the constraints imposed by the available hardware resource, they have to be programmed by off-chip software with awareness of those constraints. The TENNLab research group at the University of Tennessee, Knoxville has recently developed the …
Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim
Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim
Masters Theses
Due to significant investment, research, and development efforts over the past decade, deep neural networks (DNNs) have achieved notable advancements in classification and regression domains. As a result, DNNs are considered valuable intellectual property for artificial intelligence providers. Prior work has demonstrated highly effective model extraction attacks which steal a DNN, dismantling the provider’s business model and paving the way for unethical or malicious activities, such as misuse of personal data, safety risks in critical systems, or spreading misinformation. This thesis explores the feasibility of model extraction attacks on mobile devices using aggregated runtime profiles as a side-channel to leak …