AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by ...AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.展开更多
齿槽转矩的削弱一直是永磁电动机研究的重点之一。本文推导了采用不等槽口宽配合时可用于分析的齿槽转矩解析表达式,研究了改变相邻槽口宽度对于气隙相对磁导率的傅里叶分解系数的影响。研究表明,使得 nz/(4p)为整数的最小的 n 为偶数时...齿槽转矩的削弱一直是永磁电动机研究的重点之一。本文推导了采用不等槽口宽配合时可用于分析的齿槽转矩解析表达式,研究了改变相邻槽口宽度对于气隙相对磁导率的傅里叶分解系数的影响。研究表明,使得 nz/(4p)为整数的最小的 n 为偶数时,可以通过改变相邻两槽的槽口宽度来减小齿槽转矩,本文推导了槽口宽度的计算公式;如果使得 nz/(4p)为整数的最小的 n 为奇数时,采用相邻两槽槽口宽度不等的方法不但不会减小齿槽转矩,反而会增大齿槽转矩。最后利用有限元法进行了验证,证明本文得出的结论是正确有效的。展开更多
基金Project supported in part by the National Key Research and Development Program of China(Grant No.2021YFA0716400)the National Natural Science Foundation of China(Grant Nos.62225405,62150027,61974080,61991443,61975093,61927811,61875104,62175126,and 62235011)+2 种基金the Ministry of Science and Technology of China(Grant Nos.2021ZD0109900 and 2021ZD0109903)the Collaborative Innovation Center of Solid-State Lighting and Energy-Saving ElectronicsTsinghua University Initiative Scientific Research Program.
文摘AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.
文摘齿槽转矩的削弱一直是永磁电动机研究的重点之一。本文推导了采用不等槽口宽配合时可用于分析的齿槽转矩解析表达式,研究了改变相邻槽口宽度对于气隙相对磁导率的傅里叶分解系数的影响。研究表明,使得 nz/(4p)为整数的最小的 n 为偶数时,可以通过改变相邻两槽的槽口宽度来减小齿槽转矩,本文推导了槽口宽度的计算公式;如果使得 nz/(4p)为整数的最小的 n 为奇数时,采用相邻两槽槽口宽度不等的方法不但不会减小齿槽转矩,反而会增大齿槽转矩。最后利用有限元法进行了验证,证明本文得出的结论是正确有效的。
