On the Robustness of Stochastic Bayesian Machines

This paper revisits the stochastic computing paradigm as a way to implement architectures dedicated to probabilistic inference. In general, it is assumed the operation over stochastic bit streams is robust with respect to radiation transient events effects. Moreover, it can be expected that leveraging the stochastic computing paradigm to implement probabilistic computations such as Bayesian inference implemented in hardware could yield an increased resilience to radiation effects comparatively to deterministic procedures. However, the practical assessment of the robustness against radiation is mandatory before considering stochastic Bayesian machines (SBMs) in hazardous environments. Results of fault injection campaigns at register transfer level provide the first evidences of the intrinsic robustness of SBMs with respect to single event upsets and single event transients.

[1]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[2]  Charles Kemp,et al.  How to Grow a Mind: Statistics, Structure, and Abstraction , 2011, Science.

[3]  Raoul Velazco,et al.  An Automated SEU Fault-Injection Method and Tool for HDL-Based Designs , 2013, IEEE Transactions on Nuclear Science.

[4]  Avi Pfeffer,et al.  Practical Probabilistic Programming , 2016, ILP.

[5]  Linda Smail,et al.  Exact and approximate inference in ProBT , 2007, Rev. d'Intelligence Artif..

[6]  E. Mazer,et al.  Evidences of stochastic Bayesian machines robustness against SEUs and SETs , 2016, 2016 16th European Conference on Radiation and Its Effects on Components and Systems (RADECS).

[7]  R. Baierlein Probability Theory: The Logic of Science , 2004 .

[8]  Jacques Droulez,et al.  Bayesian sensor fusion with fast and low power stochastic circuits , 2016, 2016 IEEE International Conference on Rebooting Computing (ICRC).

[9]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[10]  Dominique Vaufreydaz,et al.  Autonomous robot controller using bitwise gibbs sampling , 2016, 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC).

[11]  M. Hassan Najafi,et al.  A Fast Fault-Tolerant Architecture for Sauvola Local Image Thresholding Algorithm Using Stochastic Computing , 2016, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[12]  Jacques Droulez,et al.  Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals , 2017, Int. J. Approx. Reason..

[13]  Jacques Droulez,et al.  Design of Stochastic Machines Dedicated to Approximate Bayesian Inferences , 2019, IEEE Transactions on Emerging Topics in Computing.

[14]  Xin Li,et al.  An Architecture for Fault-Tolerant Computation with Stochastic Logic , 2011, IEEE Transactions on Computers.

[15]  John P. Hayes,et al.  Survey of Stochastic Computing , 2013, TECS.

[16]  Stuart J. Russell,et al.  BLOG: Probabilistic Models with Unknown Objects , 2005, IJCAI.

[17]  Brian R. Gaines,et al.  Stochastic Computing Systems , 1969 .

[18]  Dominique Vaufreydaz,et al.  Stochastic Bayesian Computation for Autonomous Robot Sensorimotor System , 2015, IROS 2015.

[19]  Raphaël Laurent,et al.  Cognitive computation: A Bayesian machine case study , 2015, 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC).

[20]  Kia Bazargan,et al.  Computation on Stochastic Bit Streams Digital Image Processing Case Studies , 2014, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[21]  John P. Hayes,et al.  Optimizing stochastic circuits for accuracy-energy tradeoffs , 2015, 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[22]  Damien Querlioz,et al.  Spintronic Nanodevices for Bioinspired Computing , 2016, Proceedings of the IEEE.

[23]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[24]  Christian Laugier,et al.  Probabilistic Reasoning and Decision Making in Sensory-Motor Systems , 2008, Springer Tracts in Advanced Robotics.

[25]  Keshab K. Parhi,et al.  Architectures for Recursive Digital Filters Using Stochastic Computing , 2016, IEEE Transactions on Signal Processing.

[26]  Peter Hazucha,et al.  Characterization of soft errors caused by single event upsets in CMOS processes , 2004, IEEE Transactions on Dependable and Secure Computing.

[27]  Kia Bazargan,et al.  Polysynchronous stochastic circuits , 2016, 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC).