Modeling the Nvidia NVDLA Machine Learning Accelerator in ACADL for Performance Predictions
Bachelor’s Thesis / Master’s Thesis / Student Research Project
Abstract
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 Nvidia NVDLA Machine Learning Accelerator 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 model.
Block diagram of the NVDLA Architecture (source)
References
- Konstantin Lübeck, Alexander Louis-Ferdinand Jung, Felix Wedlich, Oliver Bringmann - Work-in-Progress: Ultra-fast yet Accurate Performance Prediction for Deep Neural Network Accelerators
- Niko Zurstraßen, Lukas Jürgen, Tim Kogel, Holger Keding, Rainer Leupers - AMAIX In-Depth: A Generic Analytical Model for Deep Learning Accelerators
Requirements
- Python
- C++
- Linux
- Deep Learning
- Successfully atteded the lecture “Grundlagen der Rechnerarchitektur” and/or “Parallele Rechnerarchitekturen” (optional)