A toolkit for reproducible hardware fault-injection experiments on PyTorch models
-
Updated
Nov 3, 2026 - Python
A toolkit for reproducible hardware fault-injection experiments on PyTorch models
An AI-powered tool that forecasts SSD/HDD hardware failures by analyzing S.M.A.R.T. data with an unsupervised anomaly detection model. Features an interactive Streamlit dashboard for real-time analysis.
SDC excitation engine for arm64 servers — a stress-ng fork that spends every cycle maximizing silent-data-corruption excitation (di/dt, SMT contention, cache/TLB/interconnect pressure, SDC-directed data shaping). Detection is delegated to SDCShield.
Open silent data corruption (SDC) tester for GPUs and AI chips, with every result in OCP Test and Validation format
Sustained fault injection for RISC-V over the DFT scan chain — flip bits in real FFs at any cycle while the processor runs
To associate your repository with the hardware-reliability topic, visit your repo's landing page and select "manage topics."