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Particle MCMC algorithms and architectures for accelerating inference in state-space models
International Journal of Approximate Reasoning ( IF 3.9 ) Pub Date : 2017-04-01 , DOI: 10.1016/j.ijar.2016.10.011 Grigorios Mingas 1 , Leonardo Bottolo 2 , Christos-Savvas Bouganis 1
International Journal of Approximate Reasoning ( IF 3.9 ) Pub Date : 2017-04-01 , DOI: 10.1016/j.ijar.2016.10.011 Grigorios Mingas 1 , Leonardo Bottolo 2 , Christos-Savvas Bouganis 1
Affiliation
Highlights • Novel algorithmic and hardware techniques for fast SSM inference are proposed.• New algorithm extends applicability of particle MCMC to multi-modal posteriors.• FPGA architectures exploit particle and chain parallelism to accelerate sampling.• 42x speedup vs. state-of-the-art CPU/GPU samplers is achieved for large problems.
中文翻译:
用于加速状态空间模型推理的粒子 MCMC 算法和架构
亮点 • 提出了用于快速 SSM 推理的新算法和硬件技术。• 新算法将粒子 MCMC 的适用性扩展到多模态后验。• FPGA 架构利用粒子和链并行性来加速采样。• 42 倍加速与状态-art CPU/GPU 采样器是针对大型问题实现的。
更新日期:2017-04-01
中文翻译:
用于加速状态空间模型推理的粒子 MCMC 算法和架构
亮点 • 提出了用于快速 SSM 推理的新算法和硬件技术。• 新算法将粒子 MCMC 的适用性扩展到多模态后验。• FPGA 架构利用粒子和链并行性来加速采样。• 42 倍加速与状态-art CPU/GPU 采样器是针对大型问题实现的。