Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines Industry Track Paper

Gan, Yiming; Bo, Yu; Tian, Boyuan; Xu, Luping; Hu, Wei; Liu, Shaoshan; Liu, Qiang; Zhang, Yanjun; Tang, Jie; Zhu, Yuhao

doi:10.1109/hpca51647.2021.00074

Cited by 17 publications

(3 citation statements)

References 57 publications

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“…Our comparisons span scalar, vector, and SLIDEX implementations for convolution (CONV) and FAST feature extraction (FAST) kernels, which make up over 40% of the application's average execution time. The baseline comprises a low-power in-order processor that resembles an ARM Cortex-A55 with Neon vector extension, commonly found in embedded and real-time systems [21]- [23]. We configured our SLIDEX implementation to be placed in the Execution stage of the CPU pipeline and to use the same L/S unit that the NEON unit uses in the baseline, with an RLEN=16 (Neon vector length) and PWO=3.…”

Section: Resultsmentioning

confidence: 99%

SLIDEX: Sliding Window Extension for Image Processing

Taranco,

Arnau,

González

2023

2023 32nd International Conference on Parallel Architectures and Compilation Techniques (PACT)

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With the rising need for efficient image processing in emerging applications such as Autonomous Driving (AD) and Augmented/Virtual Reality (AR/VR), many existing solutions do not meet their performance and energy efficiency requirements or are domain-specific and lack generality. In this work, we introduce SLIDEX, a novel ISA extension that leverages Sliding Window Processing (SWP) to bridge this gap in the image processing domain. SWP is a novel SIMD model that exposes to the programmer and natively manipulates vector registers as groups of overlapped windows of pixels to exploit the sliding-window dataflow found in convolutions and other stencil operations. SWP amplifies the available Data Level Parallelism (DLP) and reduces memory and register file accesses. We evaluated SLIDEX benefits in the critical image processing task of a state-of-the-art visual localization system widely used in AD and AR/VR. SLIDEX obtains an ∼1.2× overall speedup and 22% energy reduction.

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Section: Resultsmentioning

confidence: 99%

SLIDEX: Sliding Window Extension for Image Processing

Taranco,

Arnau,

González

2023

2023 32nd International Conference on Parallel Architectures and Compilation Techniques (PACT)

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“…The front-end and back-end consume too much BRAM to be fitted together in one Zynq FPGA chip. To solve this problem, we take a holistic approach to optimize the on-chip memory and logic of the SLAM system (will appear in [26]).…”

Section: Evaluation Resultsmentioning

confidence: 99%

On Designing Computing Systems for Autonomous Vehicles: a PerceptIn Case Study

Bo¹,

Tang²,

Liu³

2020

Preprint

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PerceptIn develops and commercializes autonomous vehicles for micromobility around the globe. This paper makes a holistic summary of PerceptIn's development and operating experiences. This paper provides the business tale behind our product, and presents the development of the computing system for our vehicles. We illustrate the design decision made for the computing system, and show the advantage of offloading localization workloads onto an FPGA platform. CCS CONCEPTS• Computer systems organization → Robotics.

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“…For instance, in outdoor environments which usually provide stable GNSS signals, the compute-light visual inertial odometry (VIO) algorithm or LiDAR odometry coupled with GNSS signals achieves the best accuracy and performance. In contrast, in unknown, unmapped indoor environments, a LiDAR or visual Simultaneous Localization and Mapping (SLAM) algorithm delivers the best accuracy [11]. Nonetheless, different localization algorithms often incur different latencies, and even worse, latency variations.…”

Section: Area 4: the Localization Systemmentioning

confidence: 99%

Rise of the Autonomous Machines

Liu¹,

Gaudiot

2022

Computer

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After decades of uninterrupted progress and growth, information technology has so evolved that it can be said we are entering the age of autonomous machines, but there exist many roadblocks in the way of making this a reality. In this article, we make a preliminary attempt at recognizing and categorizing the technical and non-technical challenges of autonomous machines; for each of the ten areas we have identified, we review current status, roadblocks, and potential research directions. It is hoped that this will help the community define clear, effective, and more formal development goalposts for the future.

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Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines Industry Track Paper

Cited by 17 publications

References 57 publications

SLIDEX: Sliding Window Extension for Image Processing

SLIDEX: Sliding Window Extension for Image Processing

On Designing Computing Systems for Autonomous Vehicles: a PerceptIn Case Study

Rise of the Autonomous Machines

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