In artificial neural networks,data structures usually exist in the form of vectors,matrices,or higher-dimensional tensors.However,traditional electronic computing architectures are limited by the bottleneck of separat...In artificial neural networks,data structures usually exist in the form of vectors,matrices,or higher-dimensional tensors.However,traditional electronic computing architectures are limited by the bottleneck of separation of storage and computing,making it difficult to efficiently handle large-scale tensor operations.The research team has developed a photonic tensor processing unit based on a single microring resonator,which performs tensor convolution operations in multiple dimensions of time,wavelength,and microwave frequency by precisely adjusting the operating state of multi-wavelength lasers.This innovative design increases the photonic computing density to 34.04 TOPS/mm²,significantly surpassing the performance level of existing photonic computing chips.展开更多
Purpose-Artificial Intelligence(AI)has surpassed expectations in opening up different possibilities for machines from different walks of life.Cloud service providers are pushing.Edge computing reduces latency,improvin...Purpose-Artificial Intelligence(AI)has surpassed expectations in opening up different possibilities for machines from different walks of life.Cloud service providers are pushing.Edge computing reduces latency,improving availability and saving bandwidth.Design/methodology/approach-The exponential growth in tensor processing unit(TPU)and graphics processing unit(GPU)combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care.A significant role of pushing and pulling data from the cloud,big data comes into play as velocity,veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record(EHR).Findings-The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence(PoP).The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.Originality/value-The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL.The scalability is at 50%with respect to the sensitivity and preservation of the PII values in the local ODL.展开更多
文摘In artificial neural networks,data structures usually exist in the form of vectors,matrices,or higher-dimensional tensors.However,traditional electronic computing architectures are limited by the bottleneck of separation of storage and computing,making it difficult to efficiently handle large-scale tensor operations.The research team has developed a photonic tensor processing unit based on a single microring resonator,which performs tensor convolution operations in multiple dimensions of time,wavelength,and microwave frequency by precisely adjusting the operating state of multi-wavelength lasers.This innovative design increases the photonic computing density to 34.04 TOPS/mm²,significantly surpassing the performance level of existing photonic computing chips.
文摘Purpose-Artificial Intelligence(AI)has surpassed expectations in opening up different possibilities for machines from different walks of life.Cloud service providers are pushing.Edge computing reduces latency,improving availability and saving bandwidth.Design/methodology/approach-The exponential growth in tensor processing unit(TPU)and graphics processing unit(GPU)combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care.A significant role of pushing and pulling data from the cloud,big data comes into play as velocity,veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record(EHR).Findings-The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence(PoP).The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.Originality/value-The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL.The scalability is at 50%with respect to the sensitivity and preservation of the PII values in the local ODL.
