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)
- Konstantin Lübeck, Alexander Louis-Ferdinand Jung, Felix Wedlich, Oliver Bringmann - Work-in-Progress: Ultra-fast yet Accurate Performance Prediction for Deep Neural Network Accelerators
- Angelo Garofalo, Giuseppe Tagliavini, Francesco Conti, Luca Benini, Davide Rossi - XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes
- Davide Rossi - PULP: Embedding AI at the Extreme Edge of the IoT @ EPFL CIS Summer School on Edge AI (Video)
- Successfully atteded the lecture “Grundlagen der Rechnerarchitektur” and/or “Parallele Rechnerarchitekturen” (optional)
- Linux (optional)