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Resilient Brain-Inspired Hyperdimensional Computing Architectures

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Short Description

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. The most important aspect of HD computing is its robustness against noise and variations in the computing platforms. Its tolerance in low signal-to-noise-ratio (SNR) conditions and for faulty components is achieved by brain-inspired properties of hypervectors: (pseudo)randomness, high-dimensionality, and fully distributed holographic representations.

In this project, your goal would be to develop an RTL implementation of an HD computing-based architecture and measure its resiliency against faulty components.

Status: Available

Looking for 1-2 Semester/Master students
Contact: Abbas Rahimi


HDL coding
Fault Injection and Testing


20% Theory
40% Architecture Design
40% Test


Luca Benini

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Detailed Task Description


Practical Details



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