{"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-neuromorphic-hardware-architecture-using","title":"A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition","arxiv_id":"1507.05695","date":"2015-07-21","proceeding":null,"authors":["Runchun Wang","Chetan Singh Thakur","Tara Julia Hamilton","Jonathan Tapson","Andre van Schaik"],"abstract":"We present a hardware architecture that uses the Neural Engineering Framework\n(NEF) to implement large-scale neural networks on Field Programmable Gate\nArrays (FPGAs) for performing pattern recognition in real time. NEF is a\nframework that is capable of synthesising large-scale cognitive systems from\nsubnetworks. We will first present the architecture of the proposed neural\nnetwork implemented using fixed-point numbers and demonstrate a routine that\ncomputes the decoding weights by using the online pseudoinverse update method\n(OPIUM) in a parallel and distributed manner. The proposed system is\nefficiently implemented on a compact digital neural core. This neural core\nconsists of 64 neurons that are instantiated by a single physical neuron using\na time-multiplexing approach. As a proof of concept, we combined 128 identical\nneural cores together to build a handwritten digit recognition system using the\nMNIST database and achieved a recognition rate of 96.55%. The system is\nimplemented on a state-of-the-art FPGA and can process 5.12 million digits per\nsecond. The architecture is not limited to handwriting recognition, but is\ngenerally applicable as an extremely fast pattern recognition processor for\nvarious kinds of patterns such as speech and images.","url_abs":"http://arxiv.org/abs/1507.05695v1","url_pdf":"http://arxiv.org/pdf/1507.05695v1.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-neuromorphic-hardware-architecture-using","repo_url":"https://github.com/Brain-Inspired-Computing/Final-Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"handwriting-recognition","task_name":"Handwriting Recognition"},{"task_slug":"handwritten-digit-recognition","task_name":"Handwritten Digit Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}