Hardware-Efficient Deconvolution-Based GAN for Edge Computing

• URL: http://arxiv.org/abs/2201.06878v1
• Date: Tue, 18 Jan 2022 11:16:59 GMT
• Title: Hardware-Efficient Deconvolution-Based GAN for Edge Computing
• Authors: Azzam Alhussain and Mingjie Lin
• Abstract summary: Generative Adversarial Networks (GAN) are cutting-edge algorithms for generating new data samples based on the learned data distribution. We proposed an HW/SW co-design approach for training quantized deconvolution GAN (QDCGAN) implemented on FPGA using a scalable streaming dataflow architecture. Various precisions, datasets, and network scalability were analyzed for low-power inference on resource-constrained platforms.
• Score: 1.5229257192293197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Adversarial Networks (GAN) are cutting-edge algorithms for generating new data samples based on the learned data distribution. However, its performance comes at a significant cost in terms of computation and memory requirements. In this paper, we proposed an HW/SW co-design approach for training quantized deconvolution GAN (QDCGAN) implemented on FPGA using a scalable streaming dataflow architecture capable of achieving higher throughput versus resource utilization trade-off. The developed accelerator is based on an efficient deconvolution engine that offers high parallelism with respect to scaling factors for GAN-based edge computing. Furthermore, various precisions, datasets, and network scalability were analyzed for low-power inference on resource-constrained platforms. Lastly, an end-to-end open-source framework is provided for training, implementation, state-space exploration, and scaling the inference using Vivado high-level synthesis for Xilinx SoC-FPGAs, and a comparison testbed with Jetson Nano.

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