Abstract
This project will lie at the intersection of two promising binary machine learning paradigms for future intelligent edge systems:
Binary Neural Networks (BNN) which use 1-bit for data representation for both weights and activations, reducing the memory footprint, while also benefiting from using binary XNOR operations and pop-count as alternatives to the dense matrix multiplication operations.
Binary Hyperdimensional Computing (HDC) whichrepresents data as high-dimensional binary vectors (e.g. of dimension 10,000) called hypervectors, and uses binary XOR, majority sum, and Hamming distance as operations. HDC is used both for pattern recognition and for reasoning tasks., and its binary variants have hardware-level approaches that result in power efficient processing for edge devices.
The goal is to produce hybrid ensembles capable of achieving high predictive performance without sacrificing their low computation footprint characteristics.
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