CMOL/CMOS Implementations of Bayesian Polytree Inference: Digital and Mixed-Signal Architectures and Performance/Price

Abstract

In this paper, we focus on aspects of the hardware implementation of the Bayesian inference framework within the George and Hawkins' model. This framework is based on Judea Pearl's belief propagation. We then present a "hardware design space exploration" methodology for implementing and analyzing the (digital and mixed-signal) hardware for the Bayesian (polytree) inference framework. This, particular, methodology involves: analyzing the computational/operational cost and the related microarchitecture, exploring candidate hardware components, proposing various custom architectures using both traditional CMOS and hybrid nanotechnology CMOS/nanowire/molecular hybrid (CMOL), and investigating the baseline performance/price of these hardware architectures. The results suggest that hybrid nanotechnology is a promising candidate to implement Bayesian inference. Such implementations utilize the very high density storage/computation benefits of these new nanoscale technologies much more efficiently, for example, the throughput per 858 mm 2 obtained for CMOL-based architectures is 32-40 times better than the TPM for a CMOS based multiprocessor/multifield-programmable gate array system, and almost 2000 times better than the TPM for a single PC implementation. In general, the assessment of such hypothetical hardware architectures provides a baseline for large-scale implementations of Bayesian inference, and guidance for implementing the same using nanogrid structures.

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“…Several hardware implementations of inference processes have been proposed in the recent past [ 41 , 50 , 81 ]. The implementation of continuous-time Bayesian inference in analog CMOS circuits, for example, has been recently discussed by Mroszczyk and Dudek [ 50 ].…”

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“…Bayesian network is modeled in form of the directed acyclic graph, the nodes represent variables in the system, the edges with the direction represents represent the causal relationship among variables, conditional probability represent the relevancy among variables, it also could be expressed and analyze the multi-source information, which helped it deal with uncertainty problems [25,26] . Both Bayesian network inference and approximate reasoning exist NP problem [27,28] , it could be reasoning efficiently in certain conditions by combining tree algorithm, Gibbs sampling algorithm, importance sampling algorithm and so on [29,30] . Bayesian network models through the following three ways: Artificial modeling method based on expert [31] , Machine learning modeling method based on sample data [32,33] and reasoning modeling method based on knowledge [34] .…”