# Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorization

arXiv: https://arxiv.org/abs/2005.03857 · HTML: https://mlsanity.com/paper/2005.03857/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 10 of 10)

- [Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis](https://mlsanity.com/paper/2406.06946/) (2024)
- [Reducing Overconfidence Predictions for Autonomous Driving Perception](https://mlsanity.com/paper/2202.07825/) (2022)
- [Spatial-SpinDrop: Spatial Dropout-based Binary Bayesian Neural Network with Spintronics Implementation](https://mlsanity.com/paper/2306.10185/) (2023)
- [Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale](https://mlsanity.com/paper/2311.15816/) (2023)
- [Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perception](https://mlsanity.com/paper/2503.20011/) (2025)
- [Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator](https://mlsanity.com/paper/2106.06048/) (2021)
- [Random-Set Graph Neural Networks](https://mlsanity.com/paper/2605.11987/) (2026)
- [Credal Graph Neural Networks](https://mlsanity.com/paper/2512.02722/) (2025)
- [Concurrent Self-testing of Neural Networks Using Uncertainty Fingerprint](https://mlsanity.com/paper/2401.01458/) (2024)
- [Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks](https://mlsanity.com/paper/2411.07902/) (2024)

## Abstract (arXiv)

Bayesian method is capable of capturing real world uncertainties/incompleteness and properly addressing the over-fitting issue faced by deep neural networks. In recent years, Bayesian Neural Networks (BNNs) have drawn tremendous attentions of AI researchers and proved to be successful in many applications. However, the required high computation complexity makes BNNs difficult to be deployed in computing systems with limited power budget. In this paper, an efficient BNN inference flow is proposed to reduce the computation cost then is evaluated by means of both software and hardware implementations. A feature decomposition and memorization (textttDM) strategy is utilized to reform the BNN inference flow in a reduced manner. About half of the computations could be eliminated compared to the traditional approach that has been proved by theoretical analysis and software validations. Subsequently, in order to resolve the hardware resource limitations, a memory-friendly computing framework is further deployed to reduce the memory overhead introduced by textttDM strategy. Finally, we implement our approach in Verilog and synthesise it with 45 nm FreePDK technology. Hardware simulation results on multi-layer BNNs demonstrate that, when compared with the traditional BNN inference method, it provides an energy consumption reduction of 73% and a 4× speedup at the expense of 14% area overhead.