Evidence map›Paper›PMID 37214316›Full record

ArticleFrontiers in neuroinformatics2023

Neuromorphic-P

Md Abdullah-Al Kaiser, Gourav Datta, Zixu Wang, Ajey P Jacob, Peter A Beerel, Akhilesh R Jaiswal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
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.

Md Abdullah-Al Kaiser *Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States.
Gourav Datta *Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States.
Zixu WangMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States.
Ajey P JacobInformation Sciences Institute, University of Southern California, Los Angeles, CA, United States.
Peter A BeerelMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States.
Akhilesh R JaiswalMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor, in-sensor, and in-pixel processing, bringing the computation closer to the sensor. In particular, in-pixel processing embeds the computation capabilities inside the pixel array and achieves high energy efficiency by generating low-level features instead of the raw data stream from CMOS image sensors. Many different in-pixel processing techniques and approaches have been demonstrated on conventional frame-based CMOS imagers; however, the processing-in-pixel approach for neuromorphic vision sensors has not been explored so far. In this work, for the first time, we propose an asynchronous non-von-Neumann analog processing-in-pixel paradigm to perform convolution operations by integrating

Indexed as

address event representationconvolutionDVS gesturehardware-algorithm co-designneuromorphicprocessing-in-pixel-in-memory

Identifiers

PMID37214316
PMCPMC10192623

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.