2018
DOI: 10.1038/s41928-018-0054-8
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Mixed-precision in-memory computing

Abstract: As CMOS scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to significantly extend the performance of today's computers. In-memory computing is a promising approach in which nanoscale resistive memory devices, organized in a computational memory unit, are used for both processing and memory. However, to reach the numerical accuracy typically required for data analytics and scientific computin… Show more

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Cited by 377 publications
(253 citation statements)
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“…Importantly, the non-volatile nature our platform, provides an exciting outlook in the development of switchable and reconfigurable metadevices by means of optical or electrical stimuli; enabling novel approaches to switchable metamaterial-based optical components (37)(38)(39).We anticipate that a plethora of novel devices and platforms should arise in the coming years, which will capitalize on the bridge between the electrical and photonic domains that are demonstrated herein. These devices potentially herald true device-level integration of hybrid optoelectronic computing platforms with in-memory computing and multilevel data storage which is readily applicable to this work (40,41).…”
Section: Resultsmentioning
confidence: 99%
“…Importantly, the non-volatile nature our platform, provides an exciting outlook in the development of switchable and reconfigurable metadevices by means of optical or electrical stimuli; enabling novel approaches to switchable metamaterial-based optical components (37)(38)(39).We anticipate that a plethora of novel devices and platforms should arise in the coming years, which will capitalize on the bridge between the electrical and photonic domains that are demonstrated herein. These devices potentially herald true device-level integration of hybrid optoelectronic computing platforms with in-memory computing and multilevel data storage which is readily applicable to this work (40,41).…”
Section: Resultsmentioning
confidence: 99%
“…Under this principle, the VMM computing process directly happened in situ, thus avoiding the memory wall (von Neumann Bottleneck) caused by fetching data from memory. Especially in supervised learning, it can reduce the computational complexity of feed forward process and backpropagation from NP to P . Therefore, current studies mostly focus on classification and regression tasks to make use of this new computing mechanism as a complement to complementary metal oxide semiconductor (CMOS) circuits.…”
Section: Introductionmentioning
confidence: 99%
“…It is commonly believed that the following types of random or sequential access memories have great potentials: static random access memory (SRAM), [8][9][10] flash memory, [11,12] magnetic random access memory (MRAM), [13,14] racetrack memory, [15,16] phase change memory (PCM), [3,[17][18][19] and resistive random access memory (RRAM). It is commonly believed that the following types of random or sequential access memories have great potentials: static random access memory (SRAM), [8][9][10] flash memory, [11,12] magnetic random access memory (MRAM), [13,14] racetrack memory, [15,16] phase change memory (PCM), [3,[17][18][19] and resistive random access memory (RRAM).…”
Section: Introductionmentioning
confidence: 99%
“…[8][9][10] Besides persistent data storage, nonvolatile memory technologies generally have a higher density, i.e., <100 F 2 cell size, where F represents the technology feature size. [18] RRAM offers versatility, including high resistivity (MΩ order of magnitude), the support of 3D integration, stochastic programming, and multilevel cell (up to 6 bits). MRAM highlights fast write speed (in the same order as SRAM) and stochastic programming.…”
Section: Introductionmentioning
confidence: 99%