BayesPerf: Minimizing Performance Monitoring Errors Using Bayesian Statistics

no code implementations • 22 Feb 2021 • Subho S. Banerjee, Saurabh Jha, Zbigniew T. Kalbarczyk, Ravishankar K. Iyer

Hardware performance counters (HPCs) that measure low-level architectural and microarchitectural events provide dynamic contextual information about the state of the system.

Decision Making Scheduling

ML-driven Malware that Targets AV Safety

no code implementations • 24 Apr 2020 • Saurabh Jha, Shengkun Cui, Subho S. Banerjee, Timothy Tsai, Zbigniew Kalbarczyk, Ravi Iyer

Ensuring the safety of autonomous vehicles (AVs) is critical for their mass deployment and public adoption.

Autonomous Driving

Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters

no code implementations • 4 Sep 2019 • Subho S. Banerjee, Saurabh Jha, Zbigniew T. Kalbarczyk, Ravishankar K. Iyer

The problem of scheduling of workloads onto heterogeneous processors (e. g., CPUs, GPUs, FPGAs) is of fundamental importance in modern data centers.

Inductive Bias reinforcement-learning +2

ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection

no code implementations • 1 Jul 2019 • Saurabh Jha, Subho S. Banerjee, Timothy Tsai, Siva K. S. Hari, Michael B. Sullivan, Zbigniew T. Kalbarczyk, Stephen W. Keckler, Ravishankar K. Iyer

The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents.

Autonomous Vehicles BIG-bench Machine Learning