research-article

ProbLP: A framework for low-precision probabilistic inference

Authors:

Laura I. Galindez Olascoaga,

Wannes Meert, and

Marian VerhelstAuthors Info & Claims

DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019

June 2019

Article No.: 190, Pages 1 - 6

https://doi.org/10.1145/3316781.3317885

Published: 02 June 2019 Publication History

Abstract

Bayesian reasoning is a powerful mechanism for probabilistic inference in smart edge-devices. During such inferences, a low-precision arithmetic representation can enable improved energy efficiency. However, its impact on inference accuracy is not yet understood. Furthermore, general-purpose hardware does not natively support low-precision representation. To address this, we propose ProbLP, a framework that automates the analysis and design of low-precision probabilistic inference hardware. It automatically chooses an appropriate energy-efficient representation based on worst-case error-bounds and hardware energy-models. It generates custom hardware for the resulting inference network exploiting parallelism, pipelining and low-precision operation. The framework is validated on several embedded-sensing benchmarks.

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Cited By

Misra ALaurel JMisailovic S(2023)ViX: Analysis-driven Compiler for Efficient Low-Precision Variational Inference2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10137324(1-6)Online publication date: Apr-2023
https://doi.org/10.23919/DATE56975.2023.10137324
Liu AWu YWang QMao ZLiu LHan JJiang H(2023)Automated Generation and Evaluation of Application-Oriented Approximate Arithmetic CircuitsDesign and Applications of Emerging Computer Systems10.1007/978-3-031-42478-6_14(353-381)Online publication date: 17-Aug-2023
https://doi.org/10.1007/978-3-031-42478-6_14
Shah NMeert WVerhelst MShah NMeert WVerhelst M(2023)DAG Processing Unit Version 1 (DPU): Efficient Execution of Irregular Workloads on a Multicore ProcessorEfficient Execution of Irregular Dataflow Graphs10.1007/978-3-031-33136-7_4(69-88)Online publication date: 26-Apr-2023
https://doi.org/10.1007/978-3-031-33136-7_4
Show More Cited By

ProbLP: A framework for low-precision probabilistic inference
1. Hardware
  1. Integrated circuits
    1. Logic circuits

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cover image ACM Conferences

DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019

June 2019

1378 pages

ISBN:9781450367257

DOI:10.1145/3316781

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGDA: ACM Special Interest Group on Design Automation
IEEE-CEDA

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SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

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Published: 02 June 2019

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DAC '19

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SIGDA

DAC '19: The 56th Annual Design Automation Conference 2019

June 2 - 6, 2019

NV, Las Vegas, USA

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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DAC '25

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62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

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Cited By

Misra ALaurel JMisailovic S(2023)ViX: Analysis-driven Compiler for Efficient Low-Precision Variational Inference2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10137324(1-6)Online publication date: Apr-2023
https://doi.org/10.23919/DATE56975.2023.10137324
Liu AWu YWang QMao ZLiu LHan JJiang H(2023)Automated Generation and Evaluation of Application-Oriented Approximate Arithmetic CircuitsDesign and Applications of Emerging Computer Systems10.1007/978-3-031-42478-6_14(353-381)Online publication date: 17-Aug-2023
https://doi.org/10.1007/978-3-031-42478-6_14
Shah NMeert WVerhelst MShah NMeert WVerhelst M(2023)DAG Processing Unit Version 1 (DPU): Efficient Execution of Irregular Workloads on a Multicore ProcessorEfficient Execution of Irregular Dataflow Graphs10.1007/978-3-031-33136-7_4(69-88)Online publication date: 26-Apr-2023
https://doi.org/10.1007/978-3-031-33136-7_4
Shah NMeert WVerhelst MShah NMeert WVerhelst M(2023)Suitable Data Representation: A Study of Fixed-Point, Floating-Point, and PositTM Formats for Probabilistic AIEfficient Execution of Irregular Dataflow Graphs10.1007/978-3-031-33136-7_2(23-41)Online publication date: 26-Apr-2023
https://doi.org/10.1007/978-3-031-33136-7_2
Shah NOlascoaga LZhao SMeert WVerhelst M(2022)DPU: DAG Processing Unit for Irregular Graphs With Precision-Scalable Posit Arithmetic in 28 nmIEEE Journal of Solid-State Circuits10.1109/JSSC.2021.313489757:8(2586-2596)Online publication date: Aug-2022
https://doi.org/10.1109/JSSC.2021.3134897
Shah NOlascoaga LZhao SMeert WVerhelst M(2021)9.4 PIU: A 248GOPS/W Stream-Based Processor for Irregular Probabilistic Inference Networks Using Precision-Scalable Posit Arithmetic in 28nm2021 IEEE International Solid- State Circuits Conference (ISSCC)10.1109/ISSCC42613.2021.9366061(150-152)Online publication date: 13-Feb-2021
https://doi.org/10.1109/ISSCC42613.2021.9366061
Laurel JYang RSehgal AUgare SMisailovic S(2021)Statheros: Compiler for Efficient Low-Precision Probabilistic Programming2021 58th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC18074.2021.9586276(787-792)Online publication date: 5-Dec-2021
https://doi.org/10.1109/DAC18074.2021.9586276
Galindez Olascoaga LMeert WVerhelst MGalindez Olascoaga LMeert WVerhelst M(2021)Hardware-Aware Probabilistic CircuitsHardware-Aware Probabilistic Machine Learning Models10.1007/978-3-030-74042-9_5(81-110)Online publication date: 12-Apr-2021
https://doi.org/10.1007/978-3-030-74042-9_5
Galindez Olascoaga LMeert WVerhelst MGalindez Olascoaga LMeert WVerhelst M(2021)Hardware-Aware Cost ModelsHardware-Aware Probabilistic Machine Learning Models10.1007/978-3-030-74042-9_3(41-53)Online publication date: 12-Apr-2021
https://doi.org/10.1007/978-3-030-74042-9_3
Galindez Olascoaga LMeert WShah NVerhelst M(2021)Dynamic Complexity Tuning for Hardware-Aware Probabilistic CircuitsIoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning10.1007/978-3-030-66770-2_21(283-295)Online publication date: 10-Jan-2021
https://doi.org/10.1007/978-3-030-66770-2_21
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