PhD - Antwerpen | More than two weeks ago
Explore the power of hybrid binary machine learning methods for flexible edge intelligence, and assess potential gains in memory and power consumption when using dedicated hardware.
This PhD project will investigate the combination of two promising binary machine learning paradigms for future intelligent edge systems:
Binary Neural Networks (BNN) use 1-bit for data representation for both weights and activations, reducing the memory footprint, while also benefiting from using binary XNOR operations and pop-count as alternatives to the dense matrix multiplication operations. Consequently, BNN are prime candidates for including machine learning capabilities in resource-constrained devices, as they save both area and power consumption. [Sayed23, Yuan23]
Binary Hyperdimensional Computing (HDC) represents data as high-dimensional binary vectors (e.g. of dimension 10,000) called hypervectors, and uses binary XOR, majority sum, and Hamming distance as operations. HDC is used both for pattern recognition and for reasoning tasks., and its binary variants have hardware-level approaches that result in power efficient processing for edge devices. [Chang23]
This PhD track will combine BNN and HDC to realize neuro-symbolic AI systems with rich internal representations, aiming for more capable and flexible AI systems.
[Sayed23] Sayed, et al, A Systematic Literature Review on Binary Neural Networks. IEEE Access. PP. 1-1. 10.1109/ACCESS.2023.3258360 (2023)
[Yuan23] Yuan, Chunyu, et al. "A comprehensive review of binary neural network." Artificial Intelligence Review (2023): 1-65
[Chang23] C. -Y. Chang, et al, "Recent Progress and Development of Hyperdimensional Computing (HDC) for Edge Intelligence," in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 13, no. 1, pp. 119-136 (2023)
[Sheth23] Sheth, A., et al, Neurosymbolic ai-why, what, and how. arXiv preprint arXiv:2305.00813 (2023)
Required background: Computer Science or equivalent
Type of work: 20% literature, 70% modeling/simulation, 10% experimental
Supervisor: Jose Antonio Oramas Mogrovejo
Co-supervisor: Steven Latre
Daily advisor: Werner Van Leekwijck
The reference code for this position is 2024-154. Mention this reference code on your application form.