Beyond von Neumann Era: Brain-inspired Hyperdimensional Computing to the Rescue

Breakthroughs in deep learning (DL) continuously fuel innovations that profoundly improve our daily life. However, DNNs overwhelm conventional computing architectures by their massive data movements between processing and memory units. As a result, novel computer architectures are indispensable to improve or even replace the decades-old von Neumann architecture. Nevertheless, going far beyond the existing von Neumann principles comes with profound reliability challenges for the performed computations. This is due to analog computing together with emerging beyond-CMOS technologies being inherently noisy and inevitably leading to unreliable computing. Hence, novel robust algorithms become a key to go beyond the boundaries of the von Neumann era. Hyper-dimensional Computing (HDC) is rapidly emerging as an attractive alternative to traditional DL and ML algorithms. Unlike conventional DL and ML algorithms, HDC is inherently robust against errors along a much more efficient hardware implementation. In addition to these advantages at hardware level, HDC's promise to learn from little data and the underlying algebra enable new possibilities at the application level. In this work, the robustness of HDC algorithms against errors and beyond von Neumann architectures are discussed. Further, the benefits of HDC as a machine learning algorithm are demonstrated with the example of outlier detection and reinforcement learning.