Journal article Open Access

Energy Efficient In-Memory Hyperdimensional Encoding for Spatio-Temporal Signal Processing

Karunaratne, Geethan; Le Gallo, Manuel; Hersche, Michael; Cherubini, Giovanni; Benini, Luca; Sebastian, Abu; Rahimi, Abbas

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Karunaratne, Geethan</dc:creator>
  <dc:creator>Le Gallo, Manuel</dc:creator>
  <dc:creator>Hersche, Michael</dc:creator>
  <dc:creator>Cherubini, Giovanni</dc:creator>
  <dc:creator>Benini, Luca</dc:creator>
  <dc:creator>Sebastian, Abu</dc:creator>
  <dc:creator>Rahimi, Abbas</dc:creator>
  <dc:description>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 1.80× energy efficiency gains, 3.36× area gains, as well as 9.74× 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.</dc:description>
  <dc:title>Energy Efficient In-Memory Hyperdimensional Encoding for Spatio-Temporal Signal Processing</dc:title>
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