Recent proliferation of CPS and IoT devices has led to an increasing demand for analyzing performance and timing of event-driven computational activity, especially interrupts and exceptions. However, these devices typically lack hardware resources, power, and system-software infrastructure for profiling/monitoring such events. Even when feasible, the profiling/monitoring activity itself can perturb the performance and timing of the timing-sensitive activity to be analyzed, therefore producing misleading results. Thus, we present PRIMER, a novel approach for profiling interrupts. PRIMER leverages existing unintentional (side-channel) electromagnetic emanations of the profiled/monitored device to identify its asynchronous execution (e.g., interrupt handlers). PRIMER leaves the monitored system (and its behavior) completely unchanged, requires no system resources or support, and introduces neither overheads nor perturbation in the monitored system. We validate PRIMER by analyzing signals that correspond to five different types of interrupts on an IoT device (ARM Cortex-M), achieving 99.5% accuracy (with no false positives), and on an MSP430 microcontroller-based device with even better accuracy. We also demonstrate the effectiveness of PRIMER in analyzing page faults and network interrupts when executing real-world applications on a more sophisticated embedded device (ARM Cortex-A8), and show that the results provided by PRIMER can provide useful insights about an application's interaction with the system's virtual memory and network-oriented services.

This paper presents EMPROF, a new method for profiling the performance impact of the memory subsystem without any support on, or interference with, the profiled system. Rather than rely on hardware support and/or software instrumentation on the profiled system, EMPROF analyzes the system's EM emanations to identify processor stalls that are associated with last-level cache (LLC) misses. This enables EMPROF to accurately pinpoint LLC misses in the execution timeline and to measure the cost (stall time) of each miss. Since EMPROF has zero "observer effect", so it can be used to profile applications that adjust their activity to their performance. It has no overhead on target machine, so it can be used for profiling embedded, hand-held, and IoT devices which usually have limited support for collecting, and limited resources for storing, the profiling data. Finally, since EMPROF can profile the system as-is, its profiling of boot code and other hard-to-profile software components is as accurate as its profiling of application code. To illustrate the effectiveness of EMPROF, we first validate its results using microbenchmarks with known memory behavior, and also on SPEC benchmarks running a cycle-accurate simulator that can provide detailed ground-truth data about LLC misses and processor stalls. We then demonstrate the effectiveness of EMPROF on real systems, including profiling of boot activity, show how its results can be attributed to the specific parts of the application code when that code is available, and provide additional insight on the statistics reported by EMPROF and how they are affected by the EM signal bandwidth provided to EMPROF.