Hyperdimensional Computing & Cognitive Memory of the Pattern Kind

Hyperdimensional computing (HDC) is an emerging computing framework that draws inspiration from attributes of the mammalian neo-cortex such as hyperdimensionality, fully distributed holographic representation, and (pseudo) randomness. When employed for machine learning tasks such as learning and classification, HDC involves manipulation and comparison of large patterns within hetero-associative persistent memory. Moreover, a key attribute of HDC is its robustness to the imperfections associated with the computational substrates on which it is implemented. It is therefore particularly amenable to emerging non-von Neumann paradigms such as in-memory computing, where the physical attributes of nanoscale emerging memory devices can be exploited to perform associative pattern computations in place. Preliminary scaled versions of this under the radar memory type have proved their value in use cases that stretch from the ultra-low power edge upward through Cloud implementations.