Difference between revisions of "Resilient Brain-Inspired Hyperdimensional Computing Architectures"
From iis-projects
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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. 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 | + | 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, allowing to implement resilient controllers. 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 | + | In this project, your goal would be to design and develop an end-to-end robust HD processor with extremely resilient controller based on principles of HD computing, and measure its resiliency against faulty components. |
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==Detailed Task Description== | ==Detailed Task Description== | ||
Revision as of 15:03, 29 January 2018
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, allowing to implement resilient controllers. 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 design and develop an end-to-end robust HD processor with extremely resilient controller based on principles of HD computing, 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
Detailed Task Description
Goals
Practical Details
Results
Links
- A Robust and Energy-Efficient Classifier Using Brain-Inspired Hyperdimensional Computing (ISLPED 2016 paper)
- High-dimensional Computing as a Nanoscalable Paradigm (TCAS 2017 paper)
- Related Matlab\VERILOG Code at GITHUB