Hardware Performance Counters: Ready-Made vs Tailor-Made

Abraham Peedikayil Kuruvila, Anushree Mahapatra, Ramesh Karri, Kanad Basu

Research output: Contribution to journalArticlepeer-review


Micro-architectural footprints can be used to distinguish one application from another. Most modern processors feature hardware performance counters to monitor the various micro-architectural events when an application is executing. These ready-made hardware performance counters can be used to create program fingerprints and have been shown to successfully differentiate between individual applications. In this paper, we demonstrate how ready-made hardware performance counters, due to their coarse-grain nature (low sampling rate and bundling of similar events, e.g., number of instructions instead of number of add instructions), are insufficient to this end. This observation motivates exploration of tailor-made hardware performance counters to capture fine-grain characteristics of the programs. As a case study, we evaluate both ready-made and tailor-made hardware performance counters using post-quantum cryptographic key encapsulation mechanism implementations. Machine learning models trained on tailor-made hardwareperformance counter streams demonstrate that they can uniquely identify the behavior of every post-quantum cryptographic key encapsulation mechanism algorithm with at least 98.99% accuracy.

Original languageEnglish (US)
Article number65
JournalACM Transactions on Embedded Computing Systems
Issue number5s
StatePublished - Oct 2021


  • Hardware performance counters
  • machine learning
  • post quantum cryptographic algorithms

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture


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