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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. HD computing is characterized by generality, scalability, and robustness against noise and variations in the computing platforms. Its tolerance for low-precision and 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

Prerequisites

HDL coding
VLSI I
Fault Injection and Testing

Character

20% Theory
40% Architecture Design
40% Test

Professor

Luca Benini

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