Exploration of uTVM and Extension to Ultra-low-power Multi-core Platforms Based on RISC-V
Parole chiave ACCELERAZIONE SOFTWARE, APPRENDIMENTO PROFONDO, BASSO CONSUMO, COMPILATORI, DISPOSITIVI INDOSSABILI, EFFICIENZA ENERGETICA, INTELLIGENZA ARTIFICIALE, MICROCONTROLLORI, RETI NEURALI CONVOLUZIONALI, RETI NEURALI PROFONDE, SISTEMI EMBEDDED, TVM
Riferimenti DANIELE JAHIER PAGLIARI
Riferimenti esterni Alessio Burrello (Politecnico di Torino)
Tipo tesi SPERIMENTALE, SVILUPPO SW
Descrizione Apache TVM is an open-source compiler for deep learning learning, able to automatically translate high-level models of a Deep Neural Network (e.g. in TensorFlow or Pytorch) to optimized machine code for a given hardware, 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 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.
Interested candidates must send an email to firstname.lastname@example.org attaching their CV and exams' transcript with scores.
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.
Scadenza validita proposta 14/12/2023 PROPONI LA TUA CANDIDATURA