While there exist many ways to deploy machine learning models on microcontrollers, it is non-trivial to choose the optimal combination of frameworks and targets for a given application.
Thus, automating the end-to-end benchmarking flow is of high relevance nowadays. MLonMCU allows performing complex benchmarks of edge ML workloads, frameworks and targets with minimal efforts
Links
Visibility
Publicly available!
License
Apache License 2.0
ISA Compliance
Status
Date of Availability
Contact
Technical University of Munich
Philipp van Kempen
Arcisstr. 21
80333 München
Deutschland
Contact Email
philipp.van-kempen [at] tum.de
Asset Reference
Asset Reference Description
Supported as Target Simulator
Asset Reference
Asset Reference Description
Supported as RISC-V kernel library
Asset Reference
Asset Reference Description
Supported as Deployment Backend
Asset Reference
Asset Reference Description
Planned to be supported as part of TVM deployment backend
Target TRL at the end of phase 1
TRL4: Versuchsaufbau im Labor --> Plattform mit Parametrisierung
Target TRL at the end of phase 2
TRL5: Versuchsaufbau in vereinfachter Einsatzumgebung --> Optimierte Plattform
Component is required for safety critical systems
Nein
Category
Compiler
Software Library