A Robust and Energy Efficient Classifier Using Brain Inspired Hyperdimensional Computing
The mathematical properties of high dimensional (HD) spaces show remarkable agreement with behaviors controlled by the brain. Computing with HD vectors, referred to as "hypervectors," is a brain inspired alternative to computing with numbers. Hypervectors are high dimensional, holographic, and (pseudo)random with independent and identically distributed (i.i.d. components. They provide for energy efficient computing while tolerating hardware variation typical of nanoscale fabrics. We describe a hardware architecture for a hypervector based classifier and demonstrate it with language identification from letter trigrams. The HD classifier is 96.7% accurate, 1.2% lower than a conventional machine learning method, operating with half the energy. Moreover, the HD classifier is able to tolerate 8.8 fold probability of failure of memory cells while maintaining 94% accuracy. This robust behavior with erroneous memory cells can significantly improve energy efficiency.
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