Resilient Brain-Inspired Hyperdimensional Computing Architectures
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.
- Looking for 1-2 Semester/Master students
- Contact: Abbas Rahimi
- HDL coding
- VLSI I
- Fault Injection and Testing
- 20% Theory
- 40% Architecture Design
- 40% Test
Detailed Task Description
- 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