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Hyperdimensional Computing

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The way the brain works suggests that rather than working with numbers that we are used to, computing with high-dimensional (HD) vectors, e.g., 10,000 bits is more efficient. Computing with HD vectors, referred to as “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 back-propagation like in neural networks). Furthermore it is memory-centric with embarrassingly parallel operations and is extremely robust against most failure mechanisms and noise. Such generality, robustness against data uncertainty, and one-shot learning make HD computing a prime candidate for utilization in application domains such as: brain-computer interfaces, biosignal processing (e.g., EEG/ECoG/EMG), robotics, voice/video classification, language recognition, text categorization, scene reasoning, analogical-based reasoning, etc.

Hypervectors are high-dimensional (e.g., 10,000 dimensions), they are (pseudo)random with independent identically distributed components and holographically distributed (i.e., not microcoded). Hypervectors can use various coding: dense or sparse, bipolar or binary and can be combined using arithmetic operations such as multiplication, addition, and permutation. The vectors can be compared for similarity using distance metrics

In this project, your goal would be to develop an RTL implementation of HD computing for an EMG-based hand gesture recognition system with fast learning using much lower power than ever before.

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