PORTALE DELLA DIDATTICA

Ricerca CERCA
  KEYWORD

Deep Learning compiler for ultra-low-power multi-core platforms based on RISC-V

Parole chiave APPRENDIMENTO PROFONDO, BASSO CONSUMO, BIOSEGNALI, COMPILATORI, DISPOSITIVI INDOSSABILI, EFFICIENZA ENERGETICA, INTELLIGENZA ARTIFICIALE, MICROCONTROLLORI, RETI NEURALI CONVOLUZIONALI, RETI NEURALI PROFONDE, SEMG, SISTEMI EMBEDDED

Riferimenti DANIELE JAHIER PAGLIARI

Riferimenti esterni Alessio Burrello (University of Bologna)

Gruppi di ricerca DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ELECTRONIC DESIGN AUTOMATION - EDA, GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA

Tipo tesi SPERIMENTALE, SVILUPPO SW

Descrizione Apache TVM is an open-source compiler for machine learning, with backend support for GPUs, CPUs, and various types of customized accelerators. Recently, the uTVM project has extended TVM support to embedded systems and low-power microcontrollers. In particular, many devices based on ARM architectures are currently supported by uTVM, which achieves comparable performance to proprietary toolchains with a reduced effort for the programmer, thanks to automation.

The goal of this thesis is to extend uTVM (or similar framework) support to embedded platforms based on the open source RISC-V instruction set. In particular, the final result of the work will be the automatic deployment, through uTVM, of complex deep learning architectures on a commercial multi-core platform based on RISC-V, and the comparison of the resulting binary performance with the ones obtained with platform-specific tools.

Conoscenze richieste Required skills include C programming and a basic understanding of compilers. Furthermore, a basic knowledge of computer architectures and embedded systems is necessary. Desired (but not required) skills include some familiarity with basic machine/deep learning concepts and corresponding models

Note Thesis in collaboration with Prof. Luca Beniniís research group at the University of Bologna and ETH Zurich. The thesis can be carried out either in Torino or in one of the other two universities.


Scadenza validita proposta 23/07/2023      PROPONI LA TUA CANDIDATURA




© Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY
Contatti