{"title":"液态硅:用于大数据/机器学习应用的具有单片集成ReRAM的非易失性完全可编程内存处理处理器","authors":"Yue Zha, E. Nowak, J. Li","doi":"10.23919/VLSIC.2019.8778064","DOIUrl":null,"url":null,"abstract":"A nonvolatile fully programmable processing-in-memory (PIM) processor named Liquid Silicon (L-Si) is demonstrated, which combines the superior programmability of general-purpose computing devices (e.g. FPGA) and the high power efficiency of do-main-specific accelerators. Besides the general computing applications, L-Si is particularly well suited for AI/machine learning and big data applications, which not only pose high computational/memory demand but also evolves rapidly. L-Si is fabricated by monolithically integrating HfO2 resistive RAM on top of commercial 130nm Si CMOS. Our measurement confirmed the fabricated chip operates reliably at low voltage of 650 mV. It achieves 60.9 TOPS/W in performing neural network inferences and 480 GOPS/W in performing content-based similarity search (a key big data application) at nominal voltage supply of 1.2V, showing >$3\\times $ and ∼$100\\times $ power efficiency improvement over the state-of-the-art domain-specific CMOS-/RRAM-based accelerators. In addition, it outperforms the latest nonvolatile FPGA in energy efficiency by ∼$3\\times $ in general compute-intensive applications.","PeriodicalId":6707,"journal":{"name":"2019 Symposium on VLSI Circuits","volume":"19 1","pages":"C206-C207"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Liquid Silicon: A Nonvolatile Fully Programmable Processing-In-Memory Processor with Monolithically Integrated ReRAM for Big Data/Machine Learning Applications\",\"authors\":\"Yue Zha, E. Nowak, J. Li\",\"doi\":\"10.23919/VLSIC.2019.8778064\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A nonvolatile fully programmable processing-in-memory (PIM) processor named Liquid Silicon (L-Si) is demonstrated, which combines the superior programmability of general-purpose computing devices (e.g. FPGA) and the high power efficiency of do-main-specific accelerators. Besides the general computing applications, L-Si is particularly well suited for AI/machine learning and big data applications, which not only pose high computational/memory demand but also evolves rapidly. L-Si is fabricated by monolithically integrating HfO2 resistive RAM on top of commercial 130nm Si CMOS. Our measurement confirmed the fabricated chip operates reliably at low voltage of 650 mV. It achieves 60.9 TOPS/W in performing neural network inferences and 480 GOPS/W in performing content-based similarity search (a key big data application) at nominal voltage supply of 1.2V, showing >$3\\\\times $ and ∼$100\\\\times $ power efficiency improvement over the state-of-the-art domain-specific CMOS-/RRAM-based accelerators. In addition, it outperforms the latest nonvolatile FPGA in energy efficiency by ∼$3\\\\times $ in general compute-intensive applications.\",\"PeriodicalId\":6707,\"journal\":{\"name\":\"2019 Symposium on VLSI Circuits\",\"volume\":\"19 1\",\"pages\":\"C206-C207\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Symposium on VLSI Circuits\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/VLSIC.2019.8778064\",\"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 Symposium on VLSI Circuits","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/VLSIC.2019.8778064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
摘要
一种非易失性的、完全可编程的内存处理(PIM)处理器——液态硅(L-Si),它结合了通用计算设备(如FPGA)优越的可编程性和专用加速器的高功率效率。除了一般的计算应用,L-Si特别适合人工智能/机器学习和大数据应用,这些应用不仅需要高计算/内存需求,而且发展迅速。L-Si是通过在商用130nm Si CMOS上单片集成HfO2电阻RAM来制造的。测试结果表明,该芯片在650 mV的低电压下工作可靠。在1.2V的额定电压下,它在执行神经网络推理方面达到60.9 TOPS/W,在执行基于内容的相似性搜索(一个关键的大数据应用)方面达到480 GOPS/W,与最先进的特定领域CMOS / rram加速器相比,显示出> 3美元和~ 100美元的功率效率提高。此外,在一般计算密集型应用中,它的能效比最新的非易失性FPGA高出约3倍。
Liquid Silicon: A Nonvolatile Fully Programmable Processing-In-Memory Processor with Monolithically Integrated ReRAM for Big Data/Machine Learning Applications
A nonvolatile fully programmable processing-in-memory (PIM) processor named Liquid Silicon (L-Si) is demonstrated, which combines the superior programmability of general-purpose computing devices (e.g. FPGA) and the high power efficiency of do-main-specific accelerators. Besides the general computing applications, L-Si is particularly well suited for AI/machine learning and big data applications, which not only pose high computational/memory demand but also evolves rapidly. L-Si is fabricated by monolithically integrating HfO2 resistive RAM on top of commercial 130nm Si CMOS. Our measurement confirmed the fabricated chip operates reliably at low voltage of 650 mV. It achieves 60.9 TOPS/W in performing neural network inferences and 480 GOPS/W in performing content-based similarity search (a key big data application) at nominal voltage supply of 1.2V, showing >$3\times $ and ∼$100\times $ power efficiency improvement over the state-of-the-art domain-specific CMOS-/RRAM-based accelerators. In addition, it outperforms the latest nonvolatile FPGA in energy efficiency by ∼$3\times $ in general compute-intensive applications.