{"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/shared-memory-contention-aware-concurrent-dnn","title":"Shared Memory-contention-aware Concurrent DNN Execution for Diversely Heterogeneous System-on-Chips","arxiv_id":"2308.05869","date":"2023-08-10","proceeding":null,"authors":["Ismet Dagli","Mehmet Belviranli"],"abstract":"Two distinguishing features of state-of-the-art mobile and autonomous systems are 1) there are often multiple workloads, mainly deep neural network (DNN) inference, running concurrently and continuously; and 2) they operate on shared memory system-on-chips (SoC) that embed heterogeneous accelerators tailored for specific operations. State-of-the-art lacks efficient performance and resource management techniques necessary to either maximize total system throughput or minimize end-to-end workload latency. In this work, we propose HaX-CoNN, a novel scheme that characterizes and maps layers in concurrently executing DNN inference workloads to a diverse set of accelerators within a SoC. Our scheme uniquely takes per-layer execution characteristics, shared memory (SM) contention, and inter-accelerator transitions into account to find optimal schedules. We evaluate HaX-CoNN on NVIDIA Orin, NVIDIA Xavier, and Qualcomm Snapdragon 865 SoCs. Our experimental results indicate that HaX-CoNN minimizes memory contention by up to 45% and can improve latency and total throughput by up to 32% and 29%, respectively, compared to the state-of-the-art approaches.","url_abs":"https://arxiv.org/abs/2308.05869v2","url_pdf":"https://arxiv.org/pdf/2308.05869v2.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":"shared-memory-contention-aware-concurrent-dnn","repo_url":"https://github.com/ismetdagli/hax-conn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"},{"task_slug":"scheduling","task_name":"Scheduling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}