TreeGRNG: Binary Tree Gaussian Random Number Generator for Efficient Probabilistic AI Hardware

Abstract:Bayesian Neural Networks (BNNs) offer opportunities for greatly enhancing the trustworthiness of conventional neural networks by monitoring the uncertainties in decision-making. A significant drawback for BNN inference at the extreme edge, however, is the imperative need to incorporate Gaussian Random Number Generators (GRNG) within each neuron. State-of-the-art GRNG algorithms heavily depend on multiple arithmetic operations and the use of extensive look-up tables, posing significant implementation challenges for ultra-low power hardware implementations. To overcome this, this paper presents an innovative binary tree random number generator (TreeGRNG) allowing the use of ultra-low-cost constant comparators instead of arithmetic units. We further enhance the TreeGRNG proposal with a set of hardware-aware optimizations exploiting the Gaussian properties. The optimized TreeGRNG surpasses the State-of-the-Art (SoTA) in terms of distribution accuracy while achieving a 3.7$\times$ reduction in energy per sample and boosting the throughput per unit area by 5.8$\times$. Moreover, our TreeGRNG proposal possesses a distinct advantage over the current SoTA in terms of flexibility, as it easily enables designers to adjust the shape of the sampled probability distribution, extending beyond the capabilities of traditional GRNGs, opening the horizon towards future probabilistic AI designs. The TreeGRNG design is available open-source in the link

Hardware Architecture (cs.AR); Machine Learning (cs.LG)

arXiv:2606.16599 [cs.AR]

(or arXiv:2606.16599v1 [cs.AR] for this version)

https://doi.org/10.48550/arXiv.2606.16599 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1-6 (2024)

https://doi.org/10.23919/DATE58400.2024.10546516 Focus to learn more DOI(s) linking to related resources

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