Reliability Estimations of Large Circuits in Massively-Parallel GPU-SPICE

SPICE simulations for reliability have special requirements. We present GPU-SPICE to serve these special requirements. First, our GPU-SPICE employs the massive parallelism found in GPUs to enable circuit simulations beyond 200K transistors. This is necessary to study reliability in microarchitecture components (e.g., multipliers, adders), as reliability estimations require full analogue SPICE simulations (instead of STA or other heuristics). Secondly, our GPU-SPICE can update transistor parameters during the circuit simulation, a feature necessary to model reliability degradation, which constantly reacts to circuit activity (e.g., Bias Temperature Instability reacting to V
_gs
changes by increasing/decreasing ΔV
_th
in each transistor). Lastly, our GPU-SPICE is open-source software, this ensures that it easily can be employed, adapted and extended by other researchers. Due to the massive parallelism in a GPU and performance optimizations (convergence criteria, CUDA memory management, etc.), our GPU-SPICE is up to 218x faster than its single-threaded baseline NGSPICE.