Energy-Efficient Simultaneous Localization and Mapping via Compounded Approximate Computing

Oh, Jinwook; Choi, Jungwook; Januario, Guilherme C.; Gopalakrishnan, Kailash

doi:10.1109/sips.2016.17

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Another recent work focuses on dynamically controlling DVFS on embedded platforms [28], but embedded platforms usually do not have the base computational power to permit turning down the voltage/frequency for visual SLAM algorithms without loss of tracking. The performance advantages of reduced-precision arithmetic in SLAM have been explored in other work [41,42]. Exploiting reduced-precision is orthogonal to our concerns in this paper.…”

Section: Related Work On Approximation In Slammentioning

confidence: 95%

A Methodology for Principled Approximation in Visual SLAM

Pei

Biswas

Fussell

et al. 2020

Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques

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This paper proposes a methodology for exploiting approximate computing to reduce the time and energy requirements of Simultaneous Localization and Mapping (SLAM) algorithms, which are used in important problem domains like robotics and autonomous driving in which autonomous agents navigate through unknown environments. Algorithms for SLAM use sensors to probe the environment, integrate this information into a map of the surroundings (mapping), and determine where the agent is in this map (localization). Visual SLAM algorithms use cameras as sensors. They can be used in places where GPS information is not available, but they have high computational requirements, leading to poor performance and high energy usage on embedded platforms. Existing studies of approximation in SLAM have mostly used offline control, which requires the trajectory be known before the agent starts to move. This is not realistic in most SLAM applications. In this paper, we present a general methodology for applying principled online approximation to visual SLAM algorithms. We implemented our proposed methodology in four visual SLAM algorithms (including one visual inertial SLAM algorithm) and evaluated them on several platforms. Our experimental results show that across different algorithms and platforms, our proposed methodology results in savings of up to 77% and 40% in computation time and energy consumption respectively with acceptable quality loss in localization and mapping accuracy over a variety of inputs. CCS CONCEPTS • Computing methodologies → Approximate computing; Control methods; Computer vision problem.

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Section: Related Work On Approximation In Slammentioning

confidence: 95%

A Methodology for Principled Approximation in Visual SLAM

Pei

Biswas

Fussell

et al. 2020

Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques

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“…The study of [19] has a high expectation of the possibility of applying approximate computing to the field of SLAM. Oh et al [20] first applied approximate computing to matrix multiplication of two major parts of RGB-SLAM: feature extraction for visual observation and robot localization using an iterative algorithm. We use a laser-based SLAM application named LittleSLAM proposed by Tomono [21] to discuss the applicability of the proposed MAC unit.…”

Section: Simultaneous Localization and Mapping (Slam)mentioning

confidence: 99%

An Accuracy Reconfigurable Vector Accelerator based on Approximate Logarithmic Multipliers for Energy-Efficient Computing

Hou

Masuda

Ishihara

2023

IEICE Trans. Fundamentals

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The approximate logarithmic multiplier proposed by Mitchell provides an efficient alternative for processing dense multiplication or multiply-accumulate operations in applications such as image processing and real-time robotics. It offers the advantages of small area, high energy efficiency and is suitable for applications that do not necessarily achieve high accuracy. However, its maximum error of 11.1% makes it challenging to deploy in applications requiring relatively high accuracy. This paper proposes a novel operand decomposition method (OD) that decomposes one multiplication into the sum of multiple approximate logarithmic multiplications to widely reduce Mitchell multiplier errors while taking full advantage of its area savings. Based on the proposed OD method, this paper also proposes an accuracy reconfigurable multiply-accumulate (MAC) unit that provides multiple reconfigurable accuracies with high parallelism. Compared to a MAC unit consisting of accurate multipliers, the area is significantly reduced to less than half, improving the hardware parallelism while satisfying the required accuracy for various scenarios. The experimental results show the excellent applicability of our proposed MAC unit in image smoothing and robot localization and mapping application. We have also designed a prototype processor that integrates the minimum functionality of this MAC unit as a vector accelerator and have implemented a software-level accuracy reconfiguration in the form of an instruction set extension. We experimentally confirmed the correct operation of the proposed vector accelerator, which provides the different degrees of accuracy and parallelism at the software level.

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“…Recent work has used KinectFusion and the SLAMBench infrastructure to study the performance impact of reducedprecision floating-point arithmetic in SLAM algorithms [25], [40]. Unlike SLAMBooster, these approaches do not exploit approximation at the algorithmic level.…”

Section: A Approximating Slammentioning

confidence: 99%

SLAMBooster: An Application-Aware Online Controller for Approximation in Dense SLAM

Pei

Biswas

Fussell

et al. 2019

2019 28th International Conference on Parallel Architectures and Compilation Techniques (PACT)

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Simultaneous Localization and Mapping (SLAM) is the problem of constructing a map of a mobile agent's environment while localizing the agent within the map. Dense SLAM algorithms perform reconstruction and localization at pixel granularity. These algorithms require a lot of computational power, which has hindered their use on low-power resourceconstrained devices.Approximate computing can be used to speed up SLAM implementations as long as the approximations do not prevent the agent from navigating correctly through the environment. Previous studies of approximation in SLAM have assumed that the entire trajectory of the agent is known before the agent starts, and they have focused on offline controllers that set approximation knobs at the start of the trajectory. In practice, the trajectory is usually not known ahead of time, and allowing knob settings to change dynamically opens up more opportunities for reducing computation time and energy.In this paper, we describe SLAMBooster, an application-aware, online control system for dense SLAM that adaptively controls approximation knobs during the motion of the agent. SLAMBooster is based on a control technique called proportional-integralderivative (PID) controller but our experiments showed this application-agnostic controller led to an unacceptable reduction in localization accuracy. To address this problem, SLAMBooster also exploits domain knowledge for controlling approximation by performing smooth surface detection and pose correction.We implemented SLAMBooster in the open-source SLAM-Bench framework and evaluated it on more than a dozen trajectories from both the literature and our own study. Our experiments show that on the average, SLAMBooster reduces the computation time by 72% and energy consumption by 35% on an embedded platform, while maintaining the accuracy of localization within reasonable bounds. These improvements make it feasible to deploy SLAM on a wider range of devices.

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Energy-Efficient Simultaneous Localization and Mapping via Compounded Approximate Computing

Cited by 6 publications

References 15 publications

A Methodology for Principled Approximation in Visual SLAM

A Methodology for Principled Approximation in Visual SLAM

An Accuracy Reconfigurable Vector Accelerator based on Approximate Logarithmic Multipliers for Energy-Efficient Computing

SLAMBooster: An Application-Aware Online Controller for Approximation in Dense SLAM

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