Until recently, FPGA-based acceleration of convolutional neural networks (CNNs) has remained an open-ended research problem. Herein, we evaluate one new method for rapidly implementing CNNs using industry-standard fra...
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ISBN:
(纸本)9798350334753
Until recently, FPGA-based acceleration of convolutional neural networks (CNNs) has remained an open-ended research problem. Herein, we evaluate one new method for rapidly implementing CNNs using industry-standard frameworks within Xilinx UltraScale+ FPGA devices. Within this workflow, referred to as Framework for Accelerating YOLO-Based ML on Edge-devices (FAYME), a TensorFlow model of the You Only Look Once version 4 (YOLOv4) object detection algorithm is realized using Xilinx's Vitis AI toolchain. We test various levels of model bit-quantization and evaluate performance while simultaneously analyzing the utilization of available memory and processing elements. We also implement a ResNet-50 model to provide additional comparisons. In this paper, we present our YOLO model, which achieves a mAP of 0.581, and our ResNet model, which achieves a Top-5 accuracy of 0.950. Furthermore, we demonstrate that these results are possible while utilizing less than 25% of the throughput offered by a single hardware accelerator in an UltraScale+ FPGA.
Object tracking has been an important research topic in the field of computer vision. At present, most target tracking algorithms need to work in an environment with good lighting conditions. Environments such as nigh...
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Recent developments have shown FPGAs to be effective for data centre applications, but debugging support in that environment has not evolved correspondingly. This presents an additional barrier to widespread adoption....
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This paper will propose a system design by using FPGA platform to implement the home automatic system in temperature and humidity control. A typical automation system will be achieved by a MCU controller, sensors, act...
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Novel applications have triggered significant changes at the system level of FPGA architecture design, such as the introduction of embedded VLIW processor arrays and hardened NoCs. However, the routing architecture of...
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The Von-Neumann bottleneck is one of the biggest problem to achieve higher computing performances and energy efficiency, especially in data centric applications. One of these application, the Internet of Things (IoT),...
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fieldprogrammablegatearrays (FPGA) are being used in a fast-growing range of scenarios, and heterogeneous CPU-FPGA systems are being tapped as a possible way to mitigate the challenges posed by the end of Moore'...
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Spiking Neural Networks (SNNs) are the next generation of Artificial Neural Networks (ANNs) that utilize an event-based representation to perform more efficient computation. Most SNN implementations have a systolic ar...
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The rapidly increasing use of IoT devices necessitates a suitable encryption algorithm for data security. The widely used Advanced Encryption Standard algorithm (AES) algorithm is computationally complex, thus unsuita...
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Encryption-especially the key exchange algorithms such as RSA-is an increasing use-model for FPGAs, driven by the adoption of the FPGA as a SmartNIC in the datacenter. While bulk encryption such as AES maps well to ge...
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