{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hardware-efficient-deconvolution-based-gan","title":"Hardware-Efficient Deconvolution-Based GAN for Edge Computing","arxiv_id":"2201.06878","date":"2022-01-18","proceeding":null,"authors":["Azzam Alhussain","Mingjie Lin"],"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.","url_abs":"https://arxiv.org/abs/2201.06878v1","url_pdf":"https://arxiv.org/pdf/2201.06878v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hardware-efficient-deconvolution-based-gan","repo_url":"https://github.com/Azzam-Alhussain/QDCGAN-PYNQ","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"hardware-efficient-deconvolution-based-gan","repo_url":"https://github.com/azzam-alhussain/fpga-qhar","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"edge-computing","task_name":"Edge-computing"},{"task_slug":"high-level-synthesis","task_name":"High-Level Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}