6 papers
Quantized AI Inference on Constrained Embedded Platforms for Small-Satellite Settings
Carlos Rafael Tordoya Taquichiri, Hans Dermot Doran, Pablo Ghiglino
In resource-constrained small-satellite settings, AI inference must operate under tight size, power, and payload budgets, which tend to limit onboard compute capability and data ha…
Achieving Dependability of AI Execution with Radiation Hardened Processors
Carlos Rafael Tordoya Taquichiri, Hans Dermot Doran, Pablo Ghiglino +1
The reliance on radiation-hardened hardware, essential for domains requiring high-dependability such as space, nuclear energy and medical applications, severely restricts the choic…
Workflow for Safe-AI
Suzana Veljanovska, Hans Dermot Doran
The development and deployment of safe and dependable AI models is crucial in applications where functional safety is a key concern. Given the rapid advancement in AI research and…
An Early-Stage Workflow Proposal for the Generation of Safe and Dependable AI Classifiers
Hans Dermot Doran, Suzana Veljanovska
The generation and execution of qualifiable safe and dependable AI models, necessitates definition of a transparent, complete yet adaptable and preferably lightweight workflow. Giv…
Performance Examination of Symbolic Aggregate Approximation in IoT Applications
Suzana Veljanovska, Hans Dermot Doran
Symbolic Aggregate approXimation (SAX) is a common dimensionality reduction approach for time-series data which has been employed in a variety of domains, including classification…
Hybrid Convolutional Neural Networks with Reliability Guarantee
Hans Dermot Doran, Suzana Veljanovska
Making AI safe and dependable requires the generation of dependable models and dependable execution of those models. We propose redundant execution as a well-known technique that c…