The unstructured data such as visual information,natural language,and human behaviors opens up a wide array of opportunities in the field of artificial intelligence(Al).The memory-centric neuromorphic computing(MNC)ha...The unstructured data such as visual information,natural language,and human behaviors opens up a wide array of opportunities in the field of artificial intelligence(Al).The memory-centric neuromorphic computing(MNC)has been proposed for the efficient processing of unstructured data,bypassing the von Neumann bottleneck of current computing architecture.The development of MNC would provide massively parallel processing of unstructured data,realizing the cognitive Al in edge and wearable systems.In this review,recent advances in memory-centric neuromorphic devices are discussed in terms of emerging nonvolatile memories,volatile switches,synaptic plasticity,neuronal models,and memristive neural network.展开更多
This article presents a comprehensive performance evaluation of Phytium 2000+,an ARMv8-based 64-core architecture.We focus on the cache and memory subsystems,analyzing the characteristics that impact the high-performa...This article presents a comprehensive performance evaluation of Phytium 2000+,an ARMv8-based 64-core architecture.We focus on the cache and memory subsystems,analyzing the characteristics that impact the high-performance computing applications.We provide insights into the memory-relevant performance behaviours of the Phytium 2000+system through micro-benchmarking.With the help of the well-known roofline model,we analyze the Phytium 2000+system,taking both memory accesses and computations into account.Based on the knowledge gained from these micro-benchmarks,we evaluate two applications and use them to assess the capabilities of the Phytium 2000+system.The results show that the ARMv8-based many-core system is capable of delivering high performance for a wide range of scientific kernels.展开更多
基金supported by Samsung Electronics Co.,Ltd(No.10201214-08153-01)supported by Convergent Technology R&D Program for Human Augmentation through the National Research Foundation of Korea(NRF)funded by Ministry of Science and ICT(No.NRF-2020M3C1B8081519)supported by the National Research Foundation of Korea(NRF)grant funded by the Korean Government(MSIP)(No.NRF-2020M3F3A2A02082445).
文摘The unstructured data such as visual information,natural language,and human behaviors opens up a wide array of opportunities in the field of artificial intelligence(Al).The memory-centric neuromorphic computing(MNC)has been proposed for the efficient processing of unstructured data,bypassing the von Neumann bottleneck of current computing architecture.The development of MNC would provide massively parallel processing of unstructured data,realizing the cognitive Al in edge and wearable systems.In this review,recent advances in memory-centric neuromorphic devices are discussed in terms of emerging nonvolatile memories,volatile switches,synaptic plasticity,neuronal models,and memristive neural network.
基金The National Key Research and Development Program of China under Grant No.2018YFB0204301the National Natural Science Foundation of China under Grant Nos.61972408 and 61602501.
文摘This article presents a comprehensive performance evaluation of Phytium 2000+,an ARMv8-based 64-core architecture.We focus on the cache and memory subsystems,analyzing the characteristics that impact the high-performance computing applications.We provide insights into the memory-relevant performance behaviours of the Phytium 2000+system through micro-benchmarking.With the help of the well-known roofline model,we analyze the Phytium 2000+system,taking both memory accesses and computations into account.Based on the knowledge gained from these micro-benchmarks,we evaluate two applications and use them to assess the capabilities of the Phytium 2000+system.The results show that the ARMv8-based many-core system is capable of delivering high performance for a wide range of scientific kernels.
