[HTML][HTML] On misbehaviour and fault tolerance in machine learning systems
Abstract Machine learning (ML) provides us with numerous opportunities, allowing ML
systems to adapt to new situations and contexts. At the same time, this adaptability raises …
systems to adapt to new situations and contexts. At the same time, this adaptability raises …
[HTML][HTML] EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems
Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under
unsupervised settings and low operational power/energy due to their bio-plausible …
unsupervised settings and low operational power/energy due to their bio-plausible …
Secure software development: industrial practice-a review
HO Nwaete - i-Manager's Journal on Software Engineering, 2022 - search.proquest.com
The current state of application assets with respect to their development, functionality,
scalability, user friendliness, and compatibility with legacy systems has witnessed an …
scalability, user friendliness, and compatibility with legacy systems has witnessed an …
Machine learning for physical layer security: Limitations, challenges and recommendation
The properties and features of wireless channels have recently attracted the attention of
researchers since they include valuable and powerful parameters for security services. This …
researchers since they include valuable and powerful parameters for security services. This …
EnforceSNN: Enabling resilient and energy-effcient spiking neural network inference considering approximate DRAMs for embedded systems.
RV Wicaksana Putra, MA Hanif… - Frontiers in …, 2022 - search.ebscohost.com
Abstract Spiking Neural Networks (SNNs) have shown capabilities of achieving high
accuracy under unsupervised settings and low operational power/energy due to their bio …
accuracy under unsupervised settings and low operational power/energy due to their bio …