{"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/extended-bit-plane-compression-for","title":"Extended Bit-Plane Compression for Convolutional Neural Network Accelerators","arxiv_id":"1810.03979","date":"2018-10-01","proceeding":null,"authors":["Lukas Cavigelli","Luca Benini"],"abstract":"After the tremendous success of convolutional neural networks in image\nclassification, object detection, speech recognition, etc., there is now rising\ndemand for deployment of these compute-intensive ML models on tightly power\nconstrained embedded and mobile systems at low cost as well as for pushing the\nthroughput in data centers. This has triggered a wave of research towards\nspecialized hardware accelerators. Their performance is often constrained by\nI/O bandwidth and the energy consumption is dominated by I/O transfers to\noff-chip memory. We introduce and evaluate a novel, hardware-friendly\ncompression scheme for the feature maps present within convolutional neural\nnetworks. We show that an average compression ratio of 4.4x relative to\nuncompressed data and a gain of 60% over existing method can be achieved for\nResNet-34 with a compression block requiring <300 bit of sequential cells and\nminimal combinational logic.","url_abs":"http://arxiv.org/abs/1810.03979v1","url_pdf":"http://arxiv.org/pdf/1810.03979v1.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":"extended-bit-plane-compression-for","repo_url":"https://github.com/lukasc-ch/ExtendedBitPlaneCompression","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}