Difference between revisions of "Resilient Brain-Inspired Hyperdimensional Computing Architectures"
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[[File:Brain_Architecture.jpg|thumb]] | [[File:Brain_Architecture.jpg|thumb]] | ||
==Short Description== | ==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 | + | 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. | 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. |
Revision as of 14:46, 29 January 2018
Contents
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
Prerequisites
- HDL coding
- VLSI I
- Fault Injection and Testing
Character
- 20% Theory
- 40% Architecture Design
- 40% Test