{"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/a-highly-parallel-fpga-implementation-of","title":"A Highly Parallel FPGA Implementation of Sparse Neural Network Training","arxiv_id":"1806.01087","date":"2018-05-31","proceeding":null,"authors":["Sourya Dey","Diandian Chen","Zongyang Li","Souvik Kundu","Kuan-Wen Huang","Keith M. Chugg","Peter A. Beerel"],"abstract":"We demonstrate an FPGA implementation of a parallel and reconfigurable\narchitecture for sparse neural networks, capable of on-chip training and\ninference. The network connectivity uses pre-determined, structured sparsity to\nsignificantly reduce complexity by lowering memory and computational\nrequirements. The architecture uses a notion of edge-processing, leading to\nefficient pipelining and parallelization. Moreover, the device can be\nreconfigured to trade off resource utilization with training time to fit\nnetworks and datasets of varying sizes. The combined effects of complexity\nreduction and easy reconfigurability enable significantly greater exploration\nof network hyperparameters and structures on-chip. As proof of concept, we show\nimplementation results on an Artix-7 FPGA.","url_abs":"http://arxiv.org/abs/1806.01087v2","url_pdf":"http://arxiv.org/pdf/1806.01087v2.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":"a-highly-parallel-fpga-implementation-of","repo_url":"https://github.com/souryadey/mlp-ondevice-training","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.01087","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}