A Hardware-oriented Approach for Efficient Bayesian Inference Computation and Deployment
Bayesian inference provides a principled foundation for reasoning under uncertainty, but its computational cost hinders deployment on resource-constrained edge devices. In this paper, we present a hardware-oriented methodology for accelerating discrete Bayesian inference on commercial off-the-shelf embedded GPUs. We identify that the latency of a broad class of variational message-passing algorithms is dominated by tensor contractions. Our approach restructures the memory layout of these operations using two complementary merging strategies that produce compact, regularly-shaped primitives better suited for efficient GPU execution. We then introduce optional sparse array representations a...
| Authors | Nikola Pižurica, Matteo Risso, Nikola Milović et al. |
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| Primary category | cs.AI |
| Published | 20 July 2026 |
| Last updated at source | 21 July 2026 |
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