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• W3006874802 startingPage "40573" @default.
• W3006874802 abstract "The advent of machine learning (ML) methods for the industry has opened new possibilities in the automotive domain, especially for Advanced Driver Assistance Systems (ADAS). These methods mainly focus on specific problems ranging from traffic sign and light recognition to pedestrian detection. In most cases, the computational resources and power budget found in ADAS systems are constrained while most machine learning methods are computationally intensive. The usual solution consists in adapting the ML models to comply with the memory and real-time (RT) requirements for inference. Some models are easily adapted to resource-constrained hardware, such as Support Vector Machines, while others, like Neural Networks, need more complex processes to fit into the desired hardware. The ADAS hardware (HW platforms) are diverse, from complex MPSoC CPUs down to classical MCUs, DPSs and application-specific FPGAs and ASICs or specific GPU platforms (such as the NVIDIA families Tegra or Jetson). Therefore, there is a tradeoff between the complexity of the ML model implemented and the selected platform that impacts the performance metrics: function results, energy consumption and speed (latency and throughput). In this paper, a survey in the form of systematic review is conducted to analyze the scope of the published research works that embed ML models into resource-constrained implementations for ADAS applications and what are the achievements regarding the ML performance, energy and speed trade-off." @default.
• W3006874802 created "2020-03-06" @default.
• W3006874802 creator A5038234965 @default.
• W3006874802 creator A5039409058 @default.
• W3006874802 creator A5056920304 @default.
• W3006874802 creator A5075562137 @default.
• W3006874802 date "2020-01-01" @default.
• W3006874802 modified "2023-10-16" @default.
• W3006874802 title "Resource-Constrained Machine Learning for ADAS: A Systematic Review" @default.
• W3006874802 cites W1529355025 @default.
• W3006874802 cites W1658425115 @default.
• W3006874802 cites W1904701389 @default.
• W3006874802 cites W1954702524 @default.
• W3006874802 cites W1964867524 @default.
• W3006874802 cites W1966756094 @default.
• W3006874802 cites W1977610018 @default.
• W3006874802 cites W1978439621 @default.
• W3006874802 cites W1989454313 @default.
• W3006874802 cites W2002427601 @default.
• W3006874802 cites W2019459561 @default.
• W3006874802 cites W2020062325 @default.
• W3006874802 cites W2037777662 @default.
• W3006874802 cites W2047030012 @default.
• W3006874802 cites W2047875689 @default.
• W3006874802 cites W2052123415 @default.
• W3006874802 cites W2056898157 @default.
• W3006874802 cites W2075596229 @default.
• W3006874802 cites W2078728791 @default.
• W3006874802 cites W2084663230 @default.
• W3006874802 cites W2098699644 @default.
• W3006874802 cites W2100488636 @default.
• W3006874802 cites W2103560185 @default.
• W3006874802 cites W2112468675 @default.
• W3006874802 cites W2124404372 @default.
• W3006874802 cites W2125874614 @default.
• W3006874802 cites W2128172975 @default.
• W3006874802 cites W2132679010 @default.
• W3006874802 cites W2141548667 @default.
• W3006874802 cites W2142704643 @default.
• W3006874802 cites W2147800946 @default.
• W3006874802 cites W2153635508 @default.
• W3006874802 cites W2161629484 @default.
• W3006874802 cites W2161969291 @default.
• W3006874802 cites W2169060624 @default.
• W3006874802 cites W2171196813 @default.
• W3006874802 cites W2191835017 @default.
• W3006874802 cites W2242976112 @default.
• W3006874802 cites W2244989617 @default.
• W3006874802 cites W2257868038 @default.
• W3006874802 cites W2260498192 @default.
• W3006874802 cites W2261250177 @default.
• W3006874802 cites W2280206420 @default.
• W3006874802 cites W2295870746 @default.
• W3006874802 cites W2340897893 @default.
• W3006874802 cites W2344951736 @default.
• W3006874802 cites W2419654905 @default.
• W3006874802 cites W2435703814 @default.
• W3006874802 cites W2478820856 @default.
• W3006874802 cites W2497335382 @default.
• W3006874802 cites W2508482253 @default.
• W3006874802 cites W2545300838 @default.
• W3006874802 cites W2559728122 @default.
• W3006874802 cites W2577160189 @default.
• W3006874802 cites W2585776351 @default.
• W3006874802 cites W2587901004 @default.
• W3006874802 cites W2601194679 @default.
• W3006874802 cites W2602041922 @default.
• W3006874802 cites W2604101178 @default.
• W3006874802 cites W2604436650 @default.
• W3006874802 cites W2649930952 @default.
• W3006874802 cites W2736585828 @default.
• W3006874802 cites W2737236189 @default.
• W3006874802 cites W2738749209 @default.
• W3006874802 cites W2741777758 @default.
• W3006874802 cites W2741922652 @default.
• W3006874802 cites W2742063911 @default.
• W3006874802 cites W2757513592 @default.
• W3006874802 cites W2768061942 @default.
• W3006874802 cites W2768408353 @default.
• W3006874802 cites W2773147029 @default.
• W3006874802 cites W2773855309 @default.
• W3006874802 cites W2785456876 @default.
• W3006874802 cites W2788097838 @default.
• W3006874802 cites W2790978696 @default.
• W3006874802 cites W2798811713 @default.
• W3006874802 cites W2799925204 @default.
• W3006874802 cites W2800257712 @default.
• W3006874802 cites W2804784163 @default.
• W3006874802 cites W2806529715 @default.
• W3006874802 cites W2806936883 @default.
• W3006874802 cites W2807450745 @default.
• W3006874802 cites W2844606858 @default.
• W3006874802 cites W2885876042 @default.
• W3006874802 cites W2888761931 @default.
• W3006874802 cites W2890690833 @default.
• W3006874802 cites W2891619122 @default.
• W3006874802 cites W2897255551 @default.
• W3006874802 cites W2898080689 @default.

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