High-throughput bayesian computing machine with reconfigurable hardware

Mingjie Lin, Ilia Lebedev, John Wawrzynek
2010 Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays - FPGA '10  
We use reconfigurable hardware to construct a high throughput Bayesian computing machine (BCM) capable of evaluating probabilistic networks with arbitrary DAG (directed acyclic graph) topology. Our BCM achieves high throughput by exploiting the FPGA's distributed memories and abundant hardware structures (such as long carry-chains and registers), which enables us to 1) develop an innovative memory allocation scheme based on a maximal matching algorithm that completely avoids memory stalls, 2)
more » ... timize and deeply pipeline the logic design of each processing node, and 3) schedule them optimally. The BCM architecture not only can be applied to many important algorithms in artificial intelligence, signal processing, and digital communications, but also has high reusability, i.e., a new application needs not change a BCM's hardware design, only new task graph processing and code compilation are necessary. Moreover, the throughput of a BCM scales almost linearly with the size of the FPGA on which it is implemented. A Bayesian computing machine with 16 processing nodes was implemented with a Virtex-5 FPGA (XCV5LX155T-2) on a BEE3 (Berkeley Emulation Engine) platform. For a wide variety of sample Bayesian problems, comparing running the same network evaluation algorithm on a 2.4 GHz Core 2 Duo Intel processor and a GeForce 9400m using the CUDA software package, the BCM demonstrates 80x and 15x speedups respectively, with a peak throughput of 20.4 GFLOPS (Giga Floating-Point Operations per Second).
doi:10.1145/1723112.1723127 dblp:conf/fpga/LinLW10 fatcat:ejmbdbecg5hjzo27j3vdn3jstq