What is it about?
It is all about designing a memory-centric architecture for hyperdimensional computing. It exploits embarrassingly parallel and localized operations and exhibits extremely robust behavior against most failure mechanisms and noise.
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Why is it important?
The way the brain works suggests that rather than working with numbers that we are used to, computing with hyperdimensional (HD) vectors, referred to as “hypervectors,” is more efficient. Computing with hypervectors, offers a general and scalable model of computing as well as well-defined set of arithmetic operations that can enable fast and one-shot learning (no need of backpropagation).
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This page is a summary of: A Robust and Energy-Efficient Classifier Using Brain-Inspired Hyperdimensional Computing, August 2016, ACM (Association for Computing Machinery),
DOI: 10.1145/2934583.2934624.
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