  The emerging brain-inspired computing paradigm known as hyperdimensional
computing (HDC) has been proven to provide a lightweight learning framework for
various cognitive tasks compared to the widely used deep learning-based
approaches. Spatio-temporal (ST) signal processing, which encompasses
biosignals such as electromyography (EMG) and electroencephalography (EEG), is
one family of applications that could benefit from an HDC-based learning
framework. At the core of HDC lie manipulations and comparisons of large bit
patterns, which are inherently ill-suited to conventional computing platforms
based on the von-Neumann architecture. In this work, we propose an architecture
for ST signal processing within the HDC framework using predominantly in-memory
compute arrays. In particular, we introduce a methodology for the in-memory
hyperdimensional encoding of ST data to be used together with an in-memory
associative search module. We show that the in-memory HDC encoder for ST
signals offers at least 1.80x energy efficiency gains, 3.36x area gains, as
well as 9.74x throughput gains compared with a dedicated digital hardware
implementation. At the same time it achieves a peak classification accuracy
within 0.04% of that of the baseline HDC framework.
