Breakthroughs in Deep neural networks (DNNs) steadily bring new innovations that substantially improve our daily life. However, DNNs overwhelm our existing computer architectures because the latter is largely bottlenecked by the data movement between memory and processing units. As a matter of fact, in the current von-Neumann architecture, which has remained unchanged since the beginning, data repeatedly moves back and forth between the physically-separated processing units (e.g., CPU, accelerator, etc.) and memory. This, in turn, inevitably leads to large latency and efficiency losses. In DNNs such a bottleneck becomes more and more prominent due to the massive amount of data that must be frequently transferred. This paper provides a cross-layer overview on how post-von-Neumann in-memory computing (IMC) architectures can be realized using three different emerging technologies: Charge-based ferroelectric transistors for logic-in-memory computations; memristive devices for unconventional brain-inspired computing; and ultra-low-power memristors especially suitable for Edge AI. Various levels of abstraction will be covered starting from semiconductor device physics to circuit and microarchitecture levels all the way up to the system level, but special attention will be put on reliability aspects.@en

Deep Learning (DL) has recently led to remark-able advancements, however, it faces severe computation related challenges. Existing Von-Neumann-based solutions are dealing with issues such as memory bandwidth limitations and energy inefficiency. Computation-In-Memory (CIM) has the potential to address this problem by integrating processing elements directly into the memory architecture, reducing data movement and enhancing the overall efficiency of the system. In this work, we propose CIM architecture using three distinct emerging technologies. Firstly, a CIM architecture utilizing Ferroelectric Field-Effect Transistors (FeFET) is shown and the resulting errors from the analog compute scheme are injected into the emerging algorithm of Hyperdimensional Computing. Subsequently, we explore Vertical Nanowire Field-Effect Transistors (VNWFETs) based CIM within a 3D computing architecture, demonstrating improved energy efficiency and reconfigurability for CIM. Additionally, we improve the accuracy of the Resistive Random Access Memories (RRAM) based CIM architecture using two mapping-based solutions. These three technologies exhibit non-volatile characteristics, and when integrated into the CIM architecture, they yield significant advantages, including enhanced energy efficiency, reliability, and accuracy in computing processes.@en