[01.06.2020] MNEMOSENE consortium partners publish authoritative research paper about In-Memory Hyperdimensional Computing in Nature Electronics
Based on results from the MNEMOSENE project, IBM Research and ETH Zurich have published an authoritative research paper about In-Memory Hyperdimensional Computing in the prestigious journal Nature Electronics.
Hyperdimensional computing is an emerging computational framework that takes inspiration from attributes of neuronal circuits including hyperdimensionality, fully distributed holographic representation, and (pseudo)randomness. When employed for machine learning tasks, such as learning and classification, the framework involves manipulation and comparison of large patterns within memory. A key attribute of hyperdimensional computing 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 approaches such as in-memory computing, where the physical attributes of nanoscale memristive devices are exploited to perform computation.
In the Nature Electronics’ paper, IBM Research and ETH Zurich report a complete in-memory hyperdimensional computing system in which all operations are implemented on two memristive crossbar engines together with peripheral digital complementary metal–oxide–semiconductor (CMOS) circuits. Their approach can achieve a near optimum trade-off between design complexity and classification accuracy based on three prototypical hyperdimensional computing related learning tasks: language classification, news classification, and hand gesture recognition from electromyography signals. Experiments using 760,000 phase-change memory devices performing analogue in-memory computing have achieved comparable accuracies to software implementations.
To read the full paper, please click on the following link.
To watch an accompanying video, please click on the following link.
To read a related IBM blog article, please click on the following link.
