Hardware performance counters (HPCs) that measure low-level architectural and microarchitectural events provide dynamic contextual information about the state of the system. However, HPC measurements are error-prone due to non determinism (e.g., undercounting due to event multiplexing, or OS interrupt-handling behaviors). In this paper, we present BayesPerf, a system for quantifying uncertainty in HPC measurements by using a domain-driven Bayesian model that captures microarchitectural relationships between HPCs to jointly infer their values as probability distributions. We provide the design and implementation of an accelerator that allows for low-latency and low-power inference of the BayesPerf model for x86 and ppc64 CPUs. BayesPerf reduces the average error in HPC measurements from 40.1% to 7.6% when events are being multiplexed. The value of BayesPerf in real-time decision-making is illustrated with a simple example of scheduling of PCIe transfers.

Items (a), (b), and (c) are integrated into a intelligence (AI) and machine learning (ML) to integrate Bayesian network (BN). BNs provide a favorable formalism mechanical, electronic, and computing technologies to make in which to model the propagation of faults across AV system real-time driving decisions. AI enables AVs to navigate through components with an interpretable model. The model, together complex environments while maintaining a safety envelope [1], with fault injection results, can be used to design and assess [2] that is continuously measured and quantified by onboard the safety of AVs. Further, BNs enable rapid probabilistic sensors (e.g., camera, LiDAR, RADAR) [3]-[5]. Clearly, the inference, which allows DriveFI to quickly find safety-critical safety and resilience of AVs are of significant concern, as faults. The Bayesian FI framework can be extended to other exemplified by several headline-making AV crashes [6], [7], safety-critical systems (e.g., surgical robots). The framework as well as prior work characterizing AV resilience during road requires specification of the safety constraints and the system tests [8]. Hence there is a compelling need for a comprehensive software architecture to model causal relationship between assessment of AV technology.