Embedded Systems

Modeling the XpulpNN Machine Learning Accelerator Instruction Set in ACADL for Performance Predictions

Bachelor’s Thesis / Master’s Thesis / Student Research Project


Abstract modeling of HW/SW systems is a relatively new research topic. This technique aims to capture only the essential parameters of software and hardware that influence their timing behavior.

This student project’s goal is to model the XpulpNN instruction set using the Python-based Abstract Computer Architecture Description Language (ACADL) and use different methods for runtime/performance prediction and compare those against the cycle-accurate hardware model.

Block diagram of the XpulpNN Architecture (source)

XpulpNN Architecture



  • Python
  • Successfully atteded the lecture “Grundlagen der Rechnerarchitektur” and/or “Parallele Rechnerarchitekturen” (optional)
  • Linux (optional)


Lübeck, Konstantin

Jung, Alexander

Bringmann, Oliver