NURSE: A Distributed Architecture for Runtime Fault Management in Processor Designs
Investigating the Impact of Soft Errors in GPU-accelerated Sparse DNNs
Progressive Training of Hybrid Clipped Rectified Linear Unit for Resilient Convolution Neural Networks
FT-Sparse: Algorithm-Based Fault Tolerance for Sparse CNNs Using Structured Sparsity in GPUs
Reliability Assessment of Deep Neural Networks and Accelerators Across Design Stages
Can Model-Level Fault Tolerance be Enough for DNN Hardware Accelerators? A Study
Special Session: Reliability Assessment of DNN Models and Inference on Systolic Arrays
Quantization-Aided Cost-Efficient Reliability of CNN Accelerators for Edge AI
AdAM: Adaptive Approximate Multiplier for Fault Tolerance in DNN Accelerators
An Efficient Architecture for Edge AI Federated Learning with Homomorphic Encryption
XMULT: An Energy-Efficient Design of Approximate Multiplier
AxEnMULT: Design of an Efficient and Reliable Approximate Encoding-Based Multiplier
European Test Symposium Teams: an Anniversary Snapshot
RL-Agent-based Early-Exit DNN Architecture Search Framework