Tutorial: The Synergy of Hyperdimensional and In-Memory Computing

Breakthroughs in deep learning consistently drive innovation. However, DNN s tend to overwhelm conventional computing systems. Hyperdimensional Computing (HDC) is rapidly gaining prominence as a potent method for rapid learning from a relatively small amount of data. It also holds the promise of offering energy-efficient lightweight computation. This tutorial will provide a comprehensive overview of the major shortcomings of existing von Neumann architectures and the growing need for innovative designs that fundamentally reduce memory latency and energy consumption by enabling data processing within the memory itself. Additionally, the tutorial will delve into the immense potential of beyond-von Neumann architectures, which utilize both emerging beyond-CMOS devices like Ferroelectric Field-Effect Transistors (FeFET) and conventional CMOS devices.