Optimizing AI at the Edge: from network topology design to MCU deployment

The first topic analyzed in the thesis will be Neural Architecture Search (NAS). I will focus on two different tools that I developed, one to optimize the architecture of Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged, and one to optimize the data precision of tensors inside CNNs. The first NAS proposed explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive field, and the number of features in each layer. Note that this is the first NAS that explicitly targets these networks. The second NAS proposed instead focuses on finding the most efficient data format for a target CNN, with the granularity of the layer filter. Note that applying these two NASes in sequence allows an "application designer" to minimize the structure of the neural network employed, minimizing the number of operations or the memory usage of the network. After that, the second topic described is the optimization of neural network deployment on edge devices. Importantly, exploiting edge platforms' scarce resources is critical for NN efficient execution on MCUs. To do so, I will introduce DORY (Deployment Oriented to memoRY) -- an automatic tool to deploy CNNs on low-cost MCUs. DORY, in different steps, can manage different levels of memory inside the MCU automatically, offload the computation workload (i.e., the different layers of a neural network) to dedicated hardware accelerators, and automatically generates ANSI C code that orchestrates off- and on-chip transfers with the computation phases. On top of this, I will introduce two optimized computation libraries that DORY can exploit to deploy TCNs and Transformers on edge efficiently. I conclude the thesis with two different applications on bio-signal analysis, i.e., heart rate tracking and sEMG-based gesture recognition.

Optimizing fused deposition modelling: the impact of geometrical slicing parameters on mechanical properties

This comprehensive study explores the intricate world of 3D printing, with a focus on Fused Deposition Modelling (FDM). It sheds light on the critical factors that influence the quality and mechanical properties of 3D printed objects. Using an optical microscope with 40X magnification, the shapes of the printed beads is correlated to specific slicing parameters, resulting in a 2D parametric model. This mathematical model, derived from real samples, serves as a tool to predict general mechanical behaviour, bridging the gap between theory and practice in FDM printing. The study begins by emphasising the importance of geometric parameters such as layer height, line width and filament tolerance on the final printed bead geometry and the resulting theoretical effect on mechanical properties. The introduction of VPratio parameter (ratio between the area of the voids and the area occupied by printed material) allows the quantification of the variation of geometric slicing parameters on the improvement or reduction of mechanical properties. The study also addresses the effect of overhang and the role of filament diameter tolerances. The research continues with the introduction of 3D FEM (Finite Element Analysis) models based on the RVE (Representative Volume Element) to verify the results obtained from the 2D model and to analyse other aspects that affect mechanical properties and not directly observable with the 2D model. The study also proposes a model for the examination of 3D printed infill structures, introducing also an innovative methodology called “double RVE” which speeds up the calculation of mechanical properties and is also more computationally efficient. Finally, the limitations of the RVE model are shown and a so-called Hybrid RVE-based model is created to overcome the limitations and inaccuracy of the conventional RVE model and homogenization procedure on some printed geometries.