Evidence map›Paper›PMID 35966373›Full record

ReviewFrontiers in neurorobotics2022

Post-silicon nano-electronic device and its application in brain-inspired chips.

Yi Lv, Houpeng Chen, Qian Wang, Xi Li, Chenchen Xie, Zhitang Song

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in neurorobotics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 1 country.

Yi LvState Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Houpeng ChenState Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Qian WangState Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Xi LiState Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Chenchen XieState Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Zhitang SongState Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Chinese Academy of Sciences · CNUniversity of Chinese Academy of Sciences · CNShanghai Institute of Microsystem and Information Technology · CNShanghai Nanotechnology Promotion Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As information technology is moving toward the era of big data, the traditional Von-Neumann architecture shows limitations in performance. The field of computing has already struggled with the latency and bandwidth required to access memory ("the memory wall") and energy dissipation ("the power wall"). These challenging issues, such as "the memory bottleneck," call for significant research investments to develop a new architecture for the next generation of computing systems. Brain-inspired computing is a new computing architecture providing a method of high energy efficiency and high real-time performance for artificial intelligence computing. Brain-inspired neural network system is based on neuron and synapse. The memristive device has been proposed as an artificial synapse for creating neuromorphic computer applications. In this study, post-silicon nano-electronic device and its application in brain-inspired chips are surveyed. First, we introduce the development of neural networks and review the current typical brain-inspired chips, including brain-inspired chips dominated by analog circuit and brain-inspired chips of the full-digital circuit, leading to the design of brain-inspired chips based on post-silicon nano-electronic device. Then, through the analysis of N kinds of post-silicon nano-electronic devices, the research progress of constructing brain-inspired chips using post-silicon nano-electronic device is expounded. Lastly, the future of building brain-inspired chips based on post-silicon nano-electronic device has been prospected.

Indexed as

brain-inspired chipsneuronphase change memorypost-silicon nano-electronic deviceresistive memorysynapse

Identifiers

PMID35966373
PMCPMC9363789
OpenAlexW4288067601

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.