Efficiency-driven optimization of deep neural network architectures
Parole chiave APPRENDIMENTO PROFONDO, BASSO CONSUMO, C, EFFICIENZA ENERGETICA, INTELLIGENZA ARTIFICIALE, MICROCONTROLLORI, RETI NEURALI, RETI NEURALI ARTIFICIALI, RETI NEURALI PROFONDE, SISTEMI EMBEDDED, SOFTWARE
Riferimenti DANIELE JAHIER PAGLIARI
Riferimenti esterni Alessio Burrello (University of Bologna)
Tipo tesi SPERIMENTALE, SVILUPPO SOFTWARE
Descrizione Nowadays, Deep Learning represents the go-to-approach to solve recognition and prediction problems in a vast spectrum of application domains, including computer vision, time-series analysis, and natural language processing. For many of these tasks, deploying the model at the edge of the IoT provides several benefits with respect to a traditional cloud-centric approach, such as predictable response times and improved privacy. However, executing complex deep neural networks (DNN) on extreme-edge devices, such as low-power microcontrollers, is complicated by their tight constraints in terms of memory and energy consumption. Therefore, bringing "intelligence" at the IoT edge requires efficient architectures, that minimize the latency/energy consumption required for an inference, without sacrificing output quality (e.g., classification accuracy). Finding these architectures manually with "trial-and-error" is tedious and costly.
Therefore, in this thesis, the candidate will explore efficient automatic optimization algorithms able to explore a vast search space of possible neural network architectures, finding the ones that yield the best accuracy versus complexity trade-off. These methods are often referred to as Neural Architecture Search (NAS) tools. In particular, the candidate will focus on key aspects of a practical NAS tool, such as: i) accurately modeling of the target hardware platform's latency and energy consumption, ii) maximizing search efficiency and minimizing search time, iii) combining NAS with other deployment-oriented neural network optimization such as quantization and mixed-precision search.
The developed tool will be general and applicable across a wide spectrum of applications. In particular, the thesis candidate will evaulate the NAS on four tasks that are relevant for edge AI (image classification, visual wake-word, speech recognition and anomaly detection), which constitute the MLPerf Tiny standard benchmark suite. In terms of deployment target, the thesis will consider extreme-edge, ultra-low-power systems, such as RISC-V-based parallel clusters of microcontrollers (e.g. GreenWaves' GAP8), and custom accelerators based on Analog-in-memory computing (AIMC).
Conoscenze richieste Required skills include C and Python programming. Further, a basic knowledge of computer architectures and embedded systems is necessary. Required skills also include some familiarity with basic machine/deep learning concepts and corresponding models.
Note Tesi in collaborazione con il gruppo di ricerca del Prof. Luca Benini presso l’Università di Bologna e l’ETH di Zurigo. È possibile sia lo svolgimento a Torino sia presso una delle altre due università.
Scadenza validita proposta 13/06/2023 PROPONI LA TUA CANDIDATURA