Flexible Computing Systems For AI Acceleration At The Extreme Edge Of The IoT

Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.

Economic preferences in the health setting. Three case studies concerning the needs of breast cancer patients, pain management in oncology and extreme end-of-life decisions

The thesis describes three studies concerning the role of the Economic Preference set investigated in the Global Preference Survey (GPS) in the following cases: 1) the needs of women with breast cancer; 2) pain undertreament in oncology; 3) legal status of euthanasia and assisted suicide. The analyses, based on regression techniques, were always conducted on the basis of aggregate data and revealed in all cases a possible role of the Economic Preferences studied, also resisting the concomitant effect of the other covariates that were considered from time to time. Regarding individual studies, the related conclusion are: 1) Economic Preferences appear to play a role in influencing the needs of women with breast cancer, albeit of non-trivial interpretation, statistically "resisting" the concomitant effect of the other independent variables considered. However, these results should be considered preliminary and need further confirmation, possibly with prospective studies conducted at the level of the individual; 2) the results show a good degree of internal consistency with regard to pro-social GPS scores, since they are all found to be non-statistically significant and united, albeit only weakly in trend, by a negative correlation with the % of pain undertreated patients. Sharper, at least statistically, is the role of Patience and Willingness to Take Risk, although of more complex empirical interpretation. 3) the results seem to indicate an obvious role of Economic Preferences, however difficult to interpret empirically. Less evidence, at least on the inferential level, emerged, however, regarding variables that, based on common sense, should play an even more obvious role than Economic Preferences in orienting attitudes toward euthanasia and assisted suicide, namely Healthcare System, Legal Origin, and Kinship Tightness; striking, in particular, is the inability to prove a role for the dominant religious orientation even with a simple bivariate analysis.