Theofilos Spyrou

SpikeFI: A Fault Injection Framework for Spiking Neural Networks

Neuromorphic computing and spiking neural networks (SNNs) are gaining traction across various artificial intelligence (AI) tasks thanks to their potential for efficient energy usage and faster...

On the Trustworthiness of Spiking Neural Networks and Neuromorphic Systems

On-Line Testing of Neuromorphic Hardware

Structural Testing Methodology for Deep Neural Networks based on RRAM

CIM-FI: A HW-Aware Fault Injection Framework for Digital Compute-In-Memory DNN Accelerators

Spiking Neuron Hardware-Level Fault Modeling

Testing and Reliability of Spiking Neural Networks: A Review of the State-of-the-Art

Analysis and Mitigation of IR Drop in Memristor-based AI Hardware Accelerators

C3CIM: Constant Column Current Memristor-Based Computation-in-Memory Micro-Architecture

Multi-Partner Project: Scalable, Ferroelectric-based Accelerators for Energy Efficient Edge AI (Ferro4EdgeAI)

Reliability Analysis of a Spiking Neural Network Hardware Accelerator

X-Sim: An Accurate and Scalable Simulator for Memristive Computing-in-Memory Accelerators

Neuron Fault Tolerance in Spiking Neural Networks

Fault Tolerant Design for Memristor-based AI Accelerators

Flexible Hardware Accelerators for Ultra-Low Power Edge AI: The CONVOLVE Approach