Bayesian Programming

Bessìère, Pierre; Mazer, Emmanuel; Ahuactzin, Juan-Manuel; Mekhnacha, Kamel

doi:10.1201/b16111

Cited by 59 publications

(62 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This type of Bayesian formalism [19] allows proper confidence estimation and combination, particularly important features when confronted with incomplete or even contradictory data coming from different sensors. A major feature of the system is its highly-parallelized design: from data fusion, to grid filtering, velocity inference and collision risk assessment, the methods have been designed to allow massive parallelization of computations, and so benefit from parallelcomputing devices [20], allowing real-time performances on embedded devices.…”

Section: A Principle Of the Cmcdotmentioning

confidence: 99%

Validation of Perception and Decision-Making Systems for Autonomous Driving via Statistical Model Checking

Barbier

Renzaglia

Quilbeuf

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Automotive systems must undergo a strict process of validation before their release on commercial vehicles. With the increased use of probabilistic approaches in autonomous systems, standard validation methods are not applicable to this end. Furthermore, real life validation, when even possible, implies costs which can be obstructive. New methods for validation and testing are thus necessary. In this paper, we propose a generic method to evaluate complex probabilistic frameworks for autonomous driving. The method is based on Statistical Model Checking (SMC), using specifically defined Key Performance Indicators (KPIs), as temporal properties depending on a set of identified metrics. By studying the behavior of these metrics during a large number of simulations via our statistical model checker, we finally evaluate the probability for the system to meet the KPIs. We show how this method can be applied to two different subsystems of an autonomous vehicle: a perception system and a decisionmaking approach. An overview of these two systems is given to understand related validation challenges. Extensive validation results are then provided for the decision-making case.

show abstract

Section: A Principle Of the Cmcdotmentioning

confidence: 99%

Validation of Perception and Decision-Making Systems for Autonomous Driving via Statistical Model Checking

Barbier

Renzaglia

Quilbeuf

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…Within *Institute of Engineering Univ. Grenoble Alpes the former Bambi project, 1 we designed several prototypes of those machines (see e.g. [12] and [7]) leading to a first generation of stochastic machines dedicated to Bayesian Inference.…”

Section: A Stochastic Machines For Probability Computation and Bayesmentioning

confidence: 99%

“…Moreover, contrary to the other above-mentioned architectures, the machine of Faix [11] is programmable: it is not necessary to map a particular program into a particular layout. A compiler translates any Bayesian program [1] into a binary code which becomes the input of a general purpose Gibbs sampler.…”

Section: B Related Workmentioning

confidence: 99%

A Bayesian Stochastic Machine for Sound Source Localization

Frisch

Laurent

Faix

et al. 2017

2017 IEEE International Conference on Rebooting Computing (ICRC)

Self Cite

View full text Add to dashboard Cite

Abstract-Compared to conventional processors, stochastic computing architectures have strong potential to speed up computation time and to reduce power consumption. We present such an architecture, called Bayesian Machine (BM), dedicated to solving Bayesian inference problems. Given a set of noisy signals provided by low-level sensors, a BM estimates the posterior probability distribution of an unknown target information. In the present study, a BM is used to solve a sound source localization (SSL) problem: the BM computes the probability distribution of the position of a sound source given acoustic signals captured by a set of microphones. Assuming free field wave propagation (no reverberations), we express the SSL problem as the maximization of a likelihood function fed with audio features provided by the time-frequency (TF) analysis of the captured audio waves. The proposed BM uses bitwise parallel sampling to fuse the resulting multi-channel information. As the number of channels to fuse is large, the standard BM architecture encounters the so-called "time dilution problem" (long delays are necessary to obtain valid samples). We tackle this problem by using max-normalization of the distributions combined with a periodic re-sampling of the bit streams after processing a reasonably small subset of evidences. Finally, we compare the localization performance of the proposed machine with the results obtained using a standard version of the machine. The re-sampling leads to an impressive acceleration factor of 10 3 in the computation.

show abstract

“…In [8] and [9] a compilation toolchain which starts from a Bayesian model described in a Bayesian programming language, namely ProBT [10], automatically designs the probabilistic machine which implements the inference over the described model. This provides the computation tree that needs to be implemented in hardware.…”

Section: The Bayesian Machine and Compilation Toolchainmentioning

confidence: 99%

“…The Bayesian machine is defined using properties, notation and formalism of Bayesian programming [10]. To design our machine we need to define the Bayesian model, the soft evidence inputs, the constant parameters of the model, and the inference to compute.…”

Section: The Bayesian Machine and Compilation Toolchainmentioning

confidence: 99%

Bayesian inference implemented on FPGA with stochastic bitstreams for an autonomous robot

Fernandes

Aslam

Lobo

et al. 2016

2016 26th International Conference on Field Programmable Logic and Applications (FPL)

View full text Add to dashboard Cite

This paper presents an FPGA implementation of a machine performing exact Bayesian inference using stochastic bitstreams. We revisited stochastic computing, not to perform better computations with unreliable hardware, but to perform approximate computations with less hardware. The underlying trade-off is between precision and computation time. An automatic design of probabilistic machines that compute soft inferences with an arithmetic based on stochastic bitstreams is presented. The computation tree provided by a Bayesian inference software is used to define the stochastic circuit. Tests were performed and results presented concerning accuracy and resource usage of the stochastic computing implementation of Bayesian machines performing exact inference. An application example is given of a Bayesian sensorimotor system that performs obstacle avoidance for an autonomous robot, fully implemented on an FPGA. Some conclusions were drawn on the followed approach, providing insights for future implementations.

show abstract

Bayesian Programming

Cited by 59 publications

References 0 publications

Validation of Perception and Decision-Making Systems for Autonomous Driving via Statistical Model Checking

Validation of Perception and Decision-Making Systems for Autonomous Driving via Statistical Model Checking

A Bayesian Stochastic Machine for Sound Source Localization

Bayesian inference implemented on FPGA with stochastic bitstreams for an autonomous robot

Contact Info

Product

Resources

About