{"title":"稀疏非局部和非结构化工作负载的脑启发内存架构","authors":"Y. Katayama","doi":"10.1145/3075564.3075597","DOIUrl":null,"url":null,"abstract":"This paper presents a brain-inspired von Neumann memory architecture for sparse, nonlocal, and unstructured workloads. Memory at each node contains selectable windows for optimistic shared access. A low-latency multiple access control for various policies is provided inside the local memory controller, using conditional deferred queuing with shared address list entries and associated lock bits. When combined with a memory-side cache, the proposed architecture is expected to transparently accelerate and flexibly scale the performance of sparse, nonlocal, and unstructured workloads by better regulating the data-access pipelining across local and remote memory requests.","PeriodicalId":398898,"journal":{"name":"Proceedings of the Computing Frontiers Conference","volume":"146 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Brain-Inspired Memory Architecture for Sparse Nonlocal and Unstructured Workloads\",\"authors\":\"Y. Katayama\",\"doi\":\"10.1145/3075564.3075597\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a brain-inspired von Neumann memory architecture for sparse, nonlocal, and unstructured workloads. Memory at each node contains selectable windows for optimistic shared access. A low-latency multiple access control for various policies is provided inside the local memory controller, using conditional deferred queuing with shared address list entries and associated lock bits. When combined with a memory-side cache, the proposed architecture is expected to transparently accelerate and flexibly scale the performance of sparse, nonlocal, and unstructured workloads by better regulating the data-access pipelining across local and remote memory requests.\",\"PeriodicalId\":398898,\"journal\":{\"name\":\"Proceedings of the Computing Frontiers Conference\",\"volume\":\"146 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Computing Frontiers Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3075564.3075597\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Computing Frontiers Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3075564.3075597","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Brain-Inspired Memory Architecture for Sparse Nonlocal and Unstructured Workloads
This paper presents a brain-inspired von Neumann memory architecture for sparse, nonlocal, and unstructured workloads. Memory at each node contains selectable windows for optimistic shared access. A low-latency multiple access control for various policies is provided inside the local memory controller, using conditional deferred queuing with shared address list entries and associated lock bits. When combined with a memory-side cache, the proposed architecture is expected to transparently accelerate and flexibly scale the performance of sparse, nonlocal, and unstructured workloads by better regulating the data-access pipelining across local and remote memory requests.