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Low-Latency In Situ Image Analytics With FPGA-Based Quantized Convolutional Neural Network
2021
IEEE Transactions on Neural Networks and Learning Systems
Real-time in situ image analytics impose stringent latency requirements on intelligent neural network inference operations. While conventional software-based implementations on the graphic processing unit (GPU)-accelerated platforms are flexible and have achieved very high inference throughput, they are not suitable for latency-sensitive applications where real-time feedback is needed. Here, we demonstrate that highperformance reconfigurable computing platforms based on fieldprogrammable gate
doi:10.1109/tnnls.2020.3046452
fatcat:uc5uzptnjjek3cbdg4b64rmvsy