Secure Machine Learning Hardware: Challenges and Progress [Feature]
With the rising adoption of deep neural networks (DNNs) for commercial and high-stakes
applications that process sensitive user data and make critical decisions, security concerns …
applications that process sensitive user data and make critical decisions, security concerns …
An energy-efficient neural network accelerator with improved resilience against fault attacks
Embedded neural network (NN) implementations are vulnerable to misclassification under
fault attacks (FAs). Clock glitching and injecting strong electromagnetic (EM) pulses are two …
fault attacks (FAs). Clock glitching and injecting strong electromagnetic (EM) pulses are two …
Neurosec: Fpga-based neuromorphic audio security
Neuromorphic systems, inspired by the complexity and functionality of the human brain,
have gained interest in academic and industrial attention due to their unparalleled potential …
have gained interest in academic and industrial attention due to their unparalleled potential …
SparseLeakyNets: Classification Prediction Attack Over Sparsity-Aware Embedded Neural Networks Using Timing Side-Channel Information
This letter explores security vulnerabilities in sparsity-aware optimizations for Neural
Network (NN) platforms, specifically focusing on timing side-channel attacks introduced by …
Network (NN) platforms, specifically focusing on timing side-channel attacks introduced by …