  Hyperdimensional computing (HDC) is an emerging computing paradigm that
exploits the distributed representation of input data in a hyperdimensional
space, the dimensions of which are typically between 1,000--10,000. The
hyperdimensional distributed representation enables energy-efficient,
low-latency, and noise-robust computations with low-precision and basic
arithmetic operations. In this study, we propose optical hyperdimensional
distributed representations based on laser speckles for adaptive, efficient,
and low-latency optical sensor processing. In the proposed approach, sensory
information is optically mapped into a hyperdimensional space with >250,000
dimensions, enabling HDC-based cognitive processing. We use this approach for
the processing of a soft-touch interface and a tactile sensor and demonstrate
to achieve high accuracy of touch or tactile recognition while significantly
reducing training data amount and computational burdens, compared with previous
machine-learning-based sensing approaches. Furthermore, we show that this
approach enables adaptive recalibration to keep high accuracy even under
different conditions.
