While the nursing community generally agrees that the Doctorate of Nursing Practice (DNP) degree will strengthen nursing as an academic discipline, there is little known about students’ perceptions of the advanced de...While the nursing community generally agrees that the Doctorate of Nursing Practice (DNP) degree will strengthen nursing as an academic discipline, there is little known about students’ perceptions of the advanced degree. Nursing students enrolled in an accelerated master’s program in nursing (N = 45) were surveyed to assess their knowledge of the DNP degree while also identifying the perceived effect a DNP might have on their careers, on nursing as a discipline, and on public perceptions of nursing practice. In this study, 51% of participants supported the transition to the DNP as the standard degree for practice nursing while 29% were opposed. The majority of participants (71%) planned to pursue an advanced practice nursing degree/certification with 81% of this group signifying that they would do so even if a DNP is required. The majority of participants agreed that the DNP will improve public perception of advanced practice nursing, but 71% thought the title of “doctor” would confuse patients. While current nursing students are generally informed of the upcoming DNP transition, there is disagreement regarding its implications for their careers and for the extent of public understanding.展开更多
Deep learning accelerators(DLAs)have been proved to be efficient computational devices for processing deep learning algorithms.Various DLA architectures are proposed and applied to different applications and tasks.How...Deep learning accelerators(DLAs)have been proved to be efficient computational devices for processing deep learning algorithms.Various DLA architectures are proposed and applied to different applications and tasks.However,for most DLAs,their programming interfaces are either difficult to use or not efficient enough.Most DLAs require programmers to directly write instructions,which is time-consuming and error-prone.Another prevailing programming interface for DLAs is high-performance libraries and deep learning frameworks,which are easy to be used and very friendly to users,but their high abstraction level limits their control capacity over the hardware resources thus compromises the efficiency of the accelerator.A design of the programming interface is for DLAs.First various existing DLAs and their programming methods are analyzed and a methodology for designing programming interface for DLAs is proposed,which is a high-level assembly language(called DLA-AL),assembler and runtime for DLAs.DLA-AL is composed of a low-level assembly language and a set of high-level blocks.It allows experienced experts to fully exploit the potential of DLAs and achieve near-optimal performance.Meanwhile,by using DLA-AL,end-users who have little knowledge of the hardware are able to develop deep learning algorithms on DLAs spending minimal programming efforts.展开更多
Recent years,the deep learning algorithm has been widely deployed from cloud servers to terminal units.And researchers proposed various neural network accelerators and software development environments.In this article...Recent years,the deep learning algorithm has been widely deployed from cloud servers to terminal units.And researchers proposed various neural network accelerators and software development environments.In this article,we have reviewed the representative neural network accelerators.As an entirety,the corresponding software stack must consider the hardware architecture of the specific accelerator to enhance the end-to-end performance.And we summarize the programming environments of neural network accelerators and optimizations in software stack.Finally,we comment the future trend of neural network accelerator and programming environments.展开更多
文摘While the nursing community generally agrees that the Doctorate of Nursing Practice (DNP) degree will strengthen nursing as an academic discipline, there is little known about students’ perceptions of the advanced degree. Nursing students enrolled in an accelerated master’s program in nursing (N = 45) were surveyed to assess their knowledge of the DNP degree while also identifying the perceived effect a DNP might have on their careers, on nursing as a discipline, and on public perceptions of nursing practice. In this study, 51% of participants supported the transition to the DNP as the standard degree for practice nursing while 29% were opposed. The majority of participants (71%) planned to pursue an advanced practice nursing degree/certification with 81% of this group signifying that they would do so even if a DNP is required. The majority of participants agreed that the DNP will improve public perception of advanced practice nursing, but 71% thought the title of “doctor” would confuse patients. While current nursing students are generally informed of the upcoming DNP transition, there is disagreement regarding its implications for their careers and for the extent of public understanding.
基金Supported by the National Key Research and Development Program of China(No.2017YFA0700902,2017YFB1003101)the 973 Program of China(No.2015CB358800)National Science and Technology Major Project(No.2018ZX01031102)
文摘Deep learning accelerators(DLAs)have been proved to be efficient computational devices for processing deep learning algorithms.Various DLA architectures are proposed and applied to different applications and tasks.However,for most DLAs,their programming interfaces are either difficult to use or not efficient enough.Most DLAs require programmers to directly write instructions,which is time-consuming and error-prone.Another prevailing programming interface for DLAs is high-performance libraries and deep learning frameworks,which are easy to be used and very friendly to users,but their high abstraction level limits their control capacity over the hardware resources thus compromises the efficiency of the accelerator.A design of the programming interface is for DLAs.First various existing DLAs and their programming methods are analyzed and a methodology for designing programming interface for DLAs is proposed,which is a high-level assembly language(called DLA-AL),assembler and runtime for DLAs.DLA-AL is composed of a low-level assembly language and a set of high-level blocks.It allows experienced experts to fully exploit the potential of DLAs and achieve near-optimal performance.Meanwhile,by using DLA-AL,end-users who have little knowledge of the hardware are able to develop deep learning algorithms on DLAs spending minimal programming efforts.
基金partially supported by the National Key Research and Development Program of China (under Grant 2017YFB1003101, 2018AAA0103300, 2017YFA0700900, 2017YFA0700902, 2017YFA0700901)the National Natural Science Foundation of China (under Grant 61732007, 61432016, 61532016, 61672491, 61602441, 61602446, 61732002, 61702478, and 61732020)+6 种基金Beijing Natural Science Foundation (JQ18013)National Science and Technology Major Project (2018ZX01031102)the Transformation and Transferof Scientific and Technological Achievements of Chinese Academy of Sciences (KFJ-HGZX-013)Key Research Projects in Frontier Science of Chinese Academy of Sciences (QYZDBSSW-JSC001)Strategic Priority Research Program of Chinese Academy of Science (XDB32050200, XDC01020000)Standardization Research Project of Chinese Academy of Sciences (BZ201800001)Beijing Academy of Artificial Intelligence (BAAI) and Beijing Nova Program of Science and Technology (Z191100001119093)
文摘Recent years,the deep learning algorithm has been widely deployed from cloud servers to terminal units.And researchers proposed various neural network accelerators and software development environments.In this article,we have reviewed the representative neural network accelerators.As an entirety,the corresponding software stack must consider the hardware architecture of the specific accelerator to enhance the end-to-end performance.And we summarize the programming environments of neural network accelerators and optimizations in software stack.Finally,we comment the future trend of neural network accelerator and programming environments.
