Hardware-Algorithm Codesign for Fast and Energy Efficient Approximate String Matching on FPGA for Computational Biology

Gudur, Venkateshwarlu Y.; Maheshwari, Sidharth; Bhardwaj, Swati; Acharyya, Amit; Shafik, Rishad

doi:10.1109/embc48229.2022.9870924

Cited by 1 publication

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“…Empirical testing of latency is important, especially for NNs architectures and and systems interfacing with external sensors. Data acquisition can already be a determinant issue [286]. For NN models, latency cannot be obtained as the inverse of throughput.…”

Section: ) Memorymentioning

confidence: 99%

A Survey of FPGA-Based Vision Systems for Autonomous Cars

et al. 2022

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On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges.INDEX TERMS autonomous car, feature extraction, field programmable gate arrays, image classification, object detection, reconfigurable architectures Self-driving functions can be implemented in several computing platforms such as ASICs, CPUs, DSPs, GPUs, FPGAs, or in heterogeneous platforms composed by any

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Section: ) Memorymentioning

confidence: 99%