$MC^2RAM$: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference Article Swipe

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Priyesh Shukla , Ahish Shylendra , Theja Tulabandhula , Amit Ranjan Trivedi ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2003.02629

This work discusses the implementation of Markov Chain Monte Carlo (MCMC) sampling from an arbitrary Gaussian mixture model (GMM) within SRAM. We show a novel architecture of SRAM by embedding it with random number generators (RNGs), digital-to-analog converters (DACs), and analog-to-digital converters (ADCs) so that SRAM arrays can be used for high performance Metropolis-Hastings (MH) algorithm-based MCMC sampling. Most of the expensive computations are performed within the SRAM and can be parallelized for high speed sampling. Our iterative compute flow minimizes data movement during sampling. We characterize power-performance trade-off of our design by simulating on 45 nm CMOS technology. For a two-dimensional, two mixture GMM, the implementation consumes ~ 91 micro-Watts power per sampling iteration and produces 500 samples in 2000 clock cycles on an average at 1 GHz clock frequency. Our study highlights interesting insights on how low-level hardware non-idealities can affect high-level sampling characteristics, and recommends ways to optimally operate SRAM within area/power constraints for high performance sampling.

Related Topics

Static Random-Access Memory

Computer Science

Monte Carlo Method

Algorithm

Electronic Engineering

Artificial Intelligence

Computer Vision

Concepts

Markov chain Monte Carlo Static random-access memory Sampling (signal processing) Computer science Monte Carlo method Algorithm CMOS Importance sampling Slice sampling Electronic engineering Bayesian probability Mathematics Engineering Computer hardware Statistics Artificial intelligence Filter (signal processing) Computer vision

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2003.02629
PDF: https://arxiv.org/pdf/2003.02629
OA Status: green
Cited By: 5
References: 14
Related Works: 20
OpenAlex ID: https://openalex.org/W3010460094

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DOI: https://doi.org/10.48550/arxiv.2003.02629

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Title: $MC^2RAM$: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-02-28

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Authors: Priyesh Shukla, Ahish Shylendra, Theja Tulabandhula, Amit Ranjan Trivedi

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Landing page: https://arxiv.org/abs/2003.02629

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Concepts: Markov chain Monte Carlo, Static random-access memory, Sampling (signal processing), Computer science, Monte Carlo method, Algorithm, CMOS, Importance sampling, Slice sampling, Electronic engineering, Bayesian probability, Mathematics, Engineering, Computer hardware, Statistics, Artificial intelligence, Filter (signal processing), Computer vision

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Cited by: 5

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Citations by year (recent): 2023: 1, 2022: 1, 2021: 2, 2020: 1

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References (count): 14

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abstract_inverted_index.novel	24
abstract_inverted_index.power	111
abstract_inverted_index.speed	74
abstract_inverted_index.study	132
abstract_inverted_index.(ADCs)	42
abstract_inverted_index.(MCMC)	10
abstract_inverted_index.Markov	6
abstract_inverted_index.affect	142
abstract_inverted_index.arrays	46
abstract_inverted_index.cycles	122
abstract_inverted_index.design	91
abstract_inverted_index.during	83
abstract_inverted_index.number	33
abstract_inverted_index.random	32
abstract_inverted_index.within	19, 65, 153
abstract_inverted_index.(DACs),	38
abstract_inverted_index.(RNGs),	35
abstract_inverted_index.average	125
abstract_inverted_index.compute	78
abstract_inverted_index.mixture	16, 103
abstract_inverted_index.operate	151
abstract_inverted_index.samples	118
abstract_inverted_index.Gaussian	15
abstract_inverted_index.consumes	107
abstract_inverted_index.hardware	139
abstract_inverted_index.insights	135
abstract_inverted_index.movement	82
abstract_inverted_index.produces	116
abstract_inverted_index.sampling	11, 113, 144
abstract_inverted_index.arbitrary	14
abstract_inverted_index.discusses	2
abstract_inverted_index.embedding	29
abstract_inverted_index.expensive	61
abstract_inverted_index.iteration	114
abstract_inverted_index.iterative	77
abstract_inverted_index.low-level	138
abstract_inverted_index.minimizes	80
abstract_inverted_index.optimally	150
abstract_inverted_index.performed	64
abstract_inverted_index.sampling.	57, 75, 84, 159
abstract_inverted_index.trade-off	88
abstract_inverted_index.area/power	154
abstract_inverted_index.converters	37, 41
abstract_inverted_index.frequency.	130
abstract_inverted_index.generators	34
abstract_inverted_index.high-level	143
abstract_inverted_index.highlights	133
abstract_inverted_index.recommends	147
abstract_inverted_index.simulating	93
abstract_inverted_index.constraints	155
abstract_inverted_index.interesting	134
abstract_inverted_index.micro-Watts	110
abstract_inverted_index.performance	52, 158
abstract_inverted_index.technology.	98
abstract_inverted_index.architecture	25
abstract_inverted_index.characterize	86
abstract_inverted_index.computations	62
abstract_inverted_index.parallelized	71
abstract_inverted_index.implementation	4, 106
abstract_inverted_index.non-idealities	140
abstract_inverted_index.algorithm-based	55
abstract_inverted_index.characteristics,	145
abstract_inverted_index.two-dimensional,	101
abstract_inverted_index.analog-to-digital	40
abstract_inverted_index.digital-to-analog	36
abstract_inverted_index.power-performance	87
abstract_inverted_index.Metropolis-Hastings	53
cited_by_percentile_year
countries_distinct_count	1
institutions_distinct_count	4
citation_normalized_percentile