{"title":"CIDAN-XE:用人工神经元在DRAM中进行计算","authors":"G. Singh, Ankit Wagle, S. Khatri, S. Vrudhula","doi":"10.3389/felec.2022.834146","DOIUrl":null,"url":null,"abstract":"This paper presents a DRAM-based processing-in-memory (PIM) architecture, called CIDAN-XE. It contains a novel computing unit called the neuron processing element (NPE). Each NPE can perform a variety of operations that include logical, arithmetic, relational, and predicate operations on multi-bit operands. Furthermore, they can be reconfigured to switch operations during run-time without increasing the overall latency or power of the operation. Since NPEs consume a small area and can operate at very high frequencies, they can be integrated inside the DRAM without disrupting its organization or timing constraints. Simulation results on a set of operations such as AND, OR, XOR, addition, multiplication, etc., show that CIDAN-XE achieves an average throughput improvement of 72X/5.4X and energy efficiency improvement of 244X/29X over CPU/GPU. To further demonstrate the benefits of using CIDAN-XE, we implement several convolutional neural networks and show that CIDAN-XE can improve upon the throughput and energy efficiency over the latest PIM architectures.","PeriodicalId":73081,"journal":{"name":"Frontiers in electronics","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"CIDAN-XE: Computing in DRAM with Artificial Neurons\",\"authors\":\"G. Singh, Ankit Wagle, S. Khatri, S. Vrudhula\",\"doi\":\"10.3389/felec.2022.834146\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a DRAM-based processing-in-memory (PIM) architecture, called CIDAN-XE. It contains a novel computing unit called the neuron processing element (NPE). Each NPE can perform a variety of operations that include logical, arithmetic, relational, and predicate operations on multi-bit operands. Furthermore, they can be reconfigured to switch operations during run-time without increasing the overall latency or power of the operation. Since NPEs consume a small area and can operate at very high frequencies, they can be integrated inside the DRAM without disrupting its organization or timing constraints. Simulation results on a set of operations such as AND, OR, XOR, addition, multiplication, etc., show that CIDAN-XE achieves an average throughput improvement of 72X/5.4X and energy efficiency improvement of 244X/29X over CPU/GPU. To further demonstrate the benefits of using CIDAN-XE, we implement several convolutional neural networks and show that CIDAN-XE can improve upon the throughput and energy efficiency over the latest PIM architectures.\",\"PeriodicalId\":73081,\"journal\":{\"name\":\"Frontiers in electronics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2022-02-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in electronics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/felec.2022.834146\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in electronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/felec.2022.834146","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
CIDAN-XE: Computing in DRAM with Artificial Neurons
This paper presents a DRAM-based processing-in-memory (PIM) architecture, called CIDAN-XE. It contains a novel computing unit called the neuron processing element (NPE). Each NPE can perform a variety of operations that include logical, arithmetic, relational, and predicate operations on multi-bit operands. Furthermore, they can be reconfigured to switch operations during run-time without increasing the overall latency or power of the operation. Since NPEs consume a small area and can operate at very high frequencies, they can be integrated inside the DRAM without disrupting its organization or timing constraints. Simulation results on a set of operations such as AND, OR, XOR, addition, multiplication, etc., show that CIDAN-XE achieves an average throughput improvement of 72X/5.4X and energy efficiency improvement of 244X/29X over CPU/GPU. To further demonstrate the benefits of using CIDAN-XE, we implement several convolutional neural networks and show that CIDAN-XE can improve upon the throughput and energy efficiency over the latest PIM architectures.