Glenn Ko, Yuji Chai, Rob Rutenbar, David Brooks, and Gu Wei. 9/8/2019. “
Accelerating Bayesian Inference on Structured Graphs Using Parallel Gibbs Sampling.” International Conference on Field-Programmable Logic and Applications. Barcelona, Spain.
Publisher's VersionAbstractBayesian models and inference is a class of machine learning that is useful for solving problems where the amount of data is scarce and prior knowledge about the application allows you to draw better conclusions. However, Bayesian models often requires computing high-dimensional integrals and finding the posterior distribution can be intractable. One of the most commonly used approximate methods for Bayesian inference is Gibbs sampling, which is a Markov chain Monte Carlo (MCMC) technique to estimate target stationary distribution. The idea in Gibbs sampling is to generate posterior samples by iterating through each of the variables to sample from its conditional given all the other variables fixed. While Gibbs sampling is a popular method for probabilistic graphical models such as Markov Random Field (MRF), the plain algorithm is slow as it goes through each of the variables sequentially. In this work, we describe a binary label MRF Gibbs sampling inference architecture and extend it to 64-label version capable of running multiple perceptual applications, such as sound source separation and stereo matching. The described accelerator employs a chromatic scheduling of variables to parallelize all the conditionally independent variables to 257 samplers, imple- mented on the FPGA portion of a CPU-FPGA SoC. For real-time streaming sound source separation task, we show the hybrid CPU- FPGA implementation is 230x faster than a commercial mobile processor, while maintaining a recommended latency under 50 ms. The 64-label version showed 137x and 679x speedups for binary label MRF Gibbs sampling inference and 64 labels, respectively.
Accelerating Bayesian Inference on Structured Graphs Using Parallel Gibbs Sampling G Ko, Yuji Chai, A Rutenbar, David Brooks, and Gu Wei. 4/28/2019. “
Flexgibbs: Reconfigurable parallel gibbs sampling accelerator for structured graphs.” In 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), Pp. 334–334.
Publisher's VersionAbstractMany consider one of the key components to the success of deep learning as its compatibility with existing accelerators, mainly GPU. While GPUs are great at handling linear algebra kernels commonly found in deep learning, they are not the optimal architecture for handling unsupervised learning methods such as Bayesian models and inference. As a step towards, achieving better understanding of architectures for probabilistic models, Gibbs sampling, one of the most commonly used algorithms for Bayesian inference, is studied with a focus on parallelism that converges to the target distribution and parameterized components. We propose FlexGibbs, a reconfigurable parallel Gibbs sampling inference accelerator for structured graphs. We designed an architecture optimal for solving Markov Random Field tasks using an array of parallel Gibbs samplers, enabled by chromatic scheduling. We show that for sound source separation application, FlexGibbs configured on the FPGA fabric of Xilinx Zync CPU-FPGA SoC achieved Gibbs sampling inference speedup of 1048x and 99.85% reduction in energy over running it on ARM Cortex-A53.
Flexgibbs: Reconfigurable parallel gibbs sampling accelerator for structured graphs