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. 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.
- 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