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.
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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
