The growing demand for real-time processing in artificial intelligence applications, particularly those involving Convolutional Neural Networks (CNNs), has highlighted the need for efficient computational solutions. Conventional processors, very often, fall short in balancing performance, power consumption, and latency, especially in embedded systems and edge computing platforms. Field-Programmable Gate Arrays (FPGAs) offer a promising alternative, combining high performance with energy efficiency and reconfigurability. The presented framework addresses the complex and demanding computations of CNNs on FPGAs maintaining full precision in all neural network parameters. Specifically, our framework is based on Darknet which is very widely used for the design of CNNs and allows the designer, by using a similar input to that given to Darknet, to efficiently implement a CNN in a heterogeneous system comprising of CPUs and FPGAs. When compared with the FPGA frameworks that support quantization, our solution aims to offer similar performance and/or energy efficiency without any degradation on the NN accuracy.

翻译：人工智能应用中对实时处理的需求日益增长，特别是涉及卷积神经网络（CNN）的应用，突显了对高效计算解决方案的需求。传统处理器往往难以在性能、功耗和延迟之间取得平衡，尤其是在嵌入式系统和边缘计算平台中。现场可编程门阵列（FPGA）提供了一种有前景的替代方案，兼具高性能、高能效和可重构性。本文提出的框架旨在解决FPGA上CNN复杂且计算密集的任务，同时保持所有神经网络参数的完整精度。具体而言，我们的框架基于广泛用于CNN设计的Darknet，允许设计者通过使用与Darknet相似的输入，在包含CPU和FPGA的异构系统中高效实现CNN。与支持量化的FPGA框架相比，我们的解决方案旨在提供相近的性能和/或能效，且不降低神经网络的精度。