Sayaka Terashima, Takuya Kojima, Hayate Okuhara, Kazusa Musha, H. Amano, Ryuichi Sakamoto, Masaaki Kondo, M. Namiki
{"title":"构建块计算系统的初步评价","authors":"Sayaka Terashima, Takuya Kojima, Hayate Okuhara, Kazusa Musha, H. Amano, Ryuichi Sakamoto, Masaaki Kondo, M. Namiki","doi":"10.1109/MCSoC.2019.00051","DOIUrl":null,"url":null,"abstract":"A building block computing system with inductive coupling Through Chip Interface (TCI) consists of 3-D chip stack, each of which is small dedicated chips. By changing the combination of stacked chips, various types of systems can be built. A MIPS R3000 compatible processor GeyserTT, a neural network accelerator SNACC and the shared memory for building the twin-tower of chips SMTT have been developed with a Renesas 65nm low leakage CMOS process. They provide the TCI IP (Intellectual Property), and an escalator network is built just by stacking them. This paper shows each chip evaluation results and performance estimation of stacking them with the RTL simulator. The performance of the single-tower and twin-tower configuration is estimated by RTL simulation when a part of Alexnet is implemented. The evaluation results showed that the single-tower configuration with GeyserTT+SNACC achieved about twice performance as the case with GeyserTT. Also, experimental results using each of the single real chip showed that all of them work at least 50MHz with extremely low power consumption. The twin-tower configuration achieved about 2x of the single-tower, that is about 6x of GeyserTT. The power consumption was about 276mW for the single-tower and 496mW for the twin-tower.","PeriodicalId":104240,"journal":{"name":"2019 IEEE 13th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A Preliminary Evaluation of Building Block Computing Systems\",\"authors\":\"Sayaka Terashima, Takuya Kojima, Hayate Okuhara, Kazusa Musha, H. Amano, Ryuichi Sakamoto, Masaaki Kondo, M. Namiki\",\"doi\":\"10.1109/MCSoC.2019.00051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A building block computing system with inductive coupling Through Chip Interface (TCI) consists of 3-D chip stack, each of which is small dedicated chips. By changing the combination of stacked chips, various types of systems can be built. A MIPS R3000 compatible processor GeyserTT, a neural network accelerator SNACC and the shared memory for building the twin-tower of chips SMTT have been developed with a Renesas 65nm low leakage CMOS process. They provide the TCI IP (Intellectual Property), and an escalator network is built just by stacking them. This paper shows each chip evaluation results and performance estimation of stacking them with the RTL simulator. The performance of the single-tower and twin-tower configuration is estimated by RTL simulation when a part of Alexnet is implemented. The evaluation results showed that the single-tower configuration with GeyserTT+SNACC achieved about twice performance as the case with GeyserTT. Also, experimental results using each of the single real chip showed that all of them work at least 50MHz with extremely low power consumption. The twin-tower configuration achieved about 2x of the single-tower, that is about 6x of GeyserTT. The power consumption was about 276mW for the single-tower and 496mW for the twin-tower.\",\"PeriodicalId\":104240,\"journal\":{\"name\":\"2019 IEEE 13th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 13th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MCSoC.2019.00051\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 13th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MCSoC.2019.00051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Preliminary Evaluation of Building Block Computing Systems
A building block computing system with inductive coupling Through Chip Interface (TCI) consists of 3-D chip stack, each of which is small dedicated chips. By changing the combination of stacked chips, various types of systems can be built. A MIPS R3000 compatible processor GeyserTT, a neural network accelerator SNACC and the shared memory for building the twin-tower of chips SMTT have been developed with a Renesas 65nm low leakage CMOS process. They provide the TCI IP (Intellectual Property), and an escalator network is built just by stacking them. This paper shows each chip evaluation results and performance estimation of stacking them with the RTL simulator. The performance of the single-tower and twin-tower configuration is estimated by RTL simulation when a part of Alexnet is implemented. The evaluation results showed that the single-tower configuration with GeyserTT+SNACC achieved about twice performance as the case with GeyserTT. Also, experimental results using each of the single real chip showed that all of them work at least 50MHz with extremely low power consumption. The twin-tower configuration achieved about 2x of the single-tower, that is about 6x of GeyserTT. The power consumption was about 276mW for the single-tower and 496mW for the twin-tower.