Modeling Processing In-Memory (PIM) 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.
In contrast to conventional processing, where data is loaded from memory and then processed by a dedicated functional unit, processing in-memory (PIM) allows for data processing inside the memory, thereby avoiding costly data movement.
This student project’s goal is to extend the Python-based Abstract Computer Architecture Description Language (ACADL) with a semantic for PIM and apply existing performance prediction methods to evaluate the use of PIM for different computer architectures.
- Konstantin Lübeck, Alexander Louis-Ferdinand Jung, Felix Wedlich, Oliver Bringmann - Work-in-Progress: Ultra-fast yet Accurate Performance Prediction for Deep Neural Network Accelerators
- Onur Mutlu, Saugata Ghose, Juan Gómez-Luna, Rachata Ausavarungnirun - A Modern Primer on Processing in Memory
- Successfully atteded the lecture “Grundlagen der Rechnerarchitektur” and/or “Parallele Rechnerarchitekturen” (optional)
- Linux (optional)