| dc.description.abstract | The research activity described in this thesis aims to demonstrate the possibility to embed Artificial
Intelligence (AI) capabilities in wearable and portable devices by deploying and executing Neural Network
(NN) models close to the sensing element. Among AI models, Deep Learning (DL) and Deep Neural Networks
can achieve high performance in many tasks, e.g. image classification, activity recognition, and so on.
However, DL models usually require a huge amount of memory resources and high-performance digital
architecture to be executed. These specifications are hardly met by wearable and portable devices, which have
to be as small as possible and guarantee a satisfactory battery lifetime. For this reason, the cloud computing
strategy is often used. However, higher latencies occur in this case, which can be unacceptable in many latencysensitive
applications, such as autonomous vehicles or assisted microsurgery. Moreover, the data transfer
consumes network bandwidth and energy. In this context, moving the computation close to the device is highly
demanded, and it is named edge-computing. However, deploying DL models on edge devices is still a
challenge. General-purpose platforms (i.e. CPUs, GPUs) are not the best solution in terms of energy efficiency,
especially for wearable and battery-powered devices, where the device lifetime is a major concern. Thus, a lot
of research is being made about the design of custom HW accelerators for DL and to move the circuitry needed
to implement the computation closer to the sensing element, thus obtaining a smart sensor. In this thesis, a
novel Hybrid Binary Neural Network (HBN) model is proposed, which exploits the advantages of Binarized
Neural Networks (BNNs). Human Activity Recognition (HAR) based on inertial sensors has been selected as
a case study. Also, a pre-processing algorithm has been developed to solve the device-orientation problem for
3-axis accelerometers. The pre-processing operations can improve the accuracy of the proposed system in
some conditions when it is used in conjunction with the HBN model. The results show an accuracy of up to
99% in recognizing 5 human activities. After having developed the model, a custom ultra-low power HW
accelerator has been designed and implemented with both FPGA and CMOS standard cells. Due to the very
low operating frequency required by HAR applications, power consumption has been reduced by reducing the
number of resources. The design can implement both the pre-processing operation and the HBN model. The
results show that the HW accelerator has a power consumption of 6.3 μW and an area occupation of 0.20 mm2
when synthesized with CMOS 65 nm Low-Power (LP) High Voltage Threshold (HVT) standard cells. The
proposed design has at least 7.3 times lower power consumption than the state-of-the-art solution. Also, a
FPGA-based demo board has been developed to demonstrate the real-time operation of the system. [edited by Author] | it_IT |
