ReviewMicromachines2023
A Review of Artificial Intelligence in Embedded Systems.
Review in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed.
- Trust-Aware Environmental State Consensus for Smart Agriculture with TEE-Enabled Sensing and Byzantine-Resilient Blockchain Coordination.Sensors (Basel, Switzerland) · 2026Article
- A Review of Embedded Artificial Intelligence Research (2023-2026): Technological Advancements, Representative Advances, and Future Prospects.Micromachines · 2026Review
- Next-generation smart ophthalmic biomaterials: From passive response to active interaction and closed-loop control.Bioactive materials · 2026Review
- Deep learning denoising enables rapid SEM imaging under charging conditions for FE SEM, CD SEM, and review SEM.Scientific reports · 2025Article
- Tools and Methods for Achieving Wi-Fi Sensing in Embedded Devices.Sensors (Basel, Switzerland) · 2025Article
- Technological Advances and Medical Applications of Implantable Electronic Devices: From the Heart, Brain, and Skin to Gastrointestinal Organs.Biosensors · 2025Review
- The Integration of Artificial Intelligence with Micro-Nano-Systems: Perspectives, Challenges and Future Prospects.Micromachines · 2025Article
- A Low-Power General Matrix Multiplication Accelerator with Sparse Weight-and-Output Stationary Dataflow.Micromachines · 2025Article
- Enhanced convolutional neural network accelerators with memory optimization for routing applications.PloS one · 2025Article
- Bidirectional Planning for Autonomous Driving Framework with Large Language Model.Sensors (Basel, Switzerland) · 2024Article
- Analysing Edge Computing Devices for the Deployment of Embedded AI.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Advancements in artificial intelligence algorithms and models, along with embedded device support, have resulted in the issue of high energy consumption and poor compatibility when deploying artificial intelligence models and networks on embedded devices becoming solvable. In response to these problems, this paper introduces three aspects of methods and applications for deploying artificial intelligence technologies on embedded devices, including artificial intelligence algorithms and models on resource-constrained hardware, acceleration methods for embedded devices, neural network compression, and current application models of embedded AI. This paper compares relevant literature, highlights the strengths and weaknesses, and concludes with future directions for embedded AI and a summary of the article.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.