Evaluation of Ferroelectric Tunnel Junction memristor for in-memory computation in real world use cases

Abstract: Machine learning algorithms are experiencing unprecedented attention, but their inherent computational complexity leads to high energy consumption. However, a paradigm shift in computing methods has the potential to address the issue. This shift could be a move towards analog in-memory computing, a method which uses Ohm’s and Kirchhoff’s Laws, and carries out the processing directly where data resides. This approach is being propelled by the development of memristors, versatile memory devices that are programmable and energy efficient. This thesis explores the capabilities of a newly engineered memristor device. This device, based on Ferroelectric Tunnel Junctions (FTJ), was developed by Lund University and presents promising technology for analog in-memory computing. In this thesis, the creation of a mathematical model took place within a simulated setting. This provided the foundation for a sensitivity analysis of chosen neural network algorithms operating on hardware featuring FTJ devices. A variety of techniques were deployed to mitigate the hardware imperfections, such as hardware-aware training, which enhanced the resilience of the algorithms. The outcomes from this investigative approach are promising, particularly regarding the inference processes in neural networks. Our research demonstrated the effectiveness of all applied mitigation techniques. The standout discovery was the robustness of the Transformer algorithm, compared to convolutional one, which proved capable of withstanding hardware imperfections while producing results on par with those of the digital model.