| Summary: | Deep Learning techniques have been successfully applied to solve many Artificial Intelligence (AI) applications problems. However, owing to topologies with many hidden layers, Deep Neural Networks (DNNs) have high computational complexity, which makes their deployment difficult in contexts highly constrained by requirements such as performance, real-time processing, or energy efficiency. Numerous hardware/software optimization techniques using GPUs, ASICs, and reconfigurable computing (i.e, FPGAs), have been proposed in the literature. With FPGAs, very specialized architectures have been developed to provide an optimal balance between high-speed and low power. However, when targeting edge computing, user requirements and hardware constraints must be efficiently met. Therefore, in this work, we only focus on reconfigurable embedded systems based on the Xilinx ZYNQ SoC and popular DNNs that can be implemented on Embedded Edge improving performance per watt while maintaining accuracy. In this context, we propose an automated framework for the implementation of hardware-accelerated DNN architectures. This framework provides an end-to-end solution that facilitates the efficient deployment of topologies on FPGAs by combining custom hardware scalability with optimization strategies. Cutting-edge comparisons and experimental results demonstrate that the architectures developed by our framework offer the best compromise between performance, energy consumption, and system costs. For instance, the low power (0.266W) DNN topologies generated for the MNIST database achieved a high throughput of 3,626 FPS.
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