Low-power ultra-small edge ai accelerators for image recognition with convolution neural networks: Analysis and future directions

Weison Lin*, Adewale Adetomi, Tughrul Arslan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Edge AI accelerators have been emerging as a solution for near customers’ applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications require meeting performance targets and resilience constraints due to the limited device area and hostile environments for operation. Numerous research articles have proposed the edge AI accelerator for satisfying the applications, but not all include full specifications. Most of them tend to compare the architecture with other existing CPUs, GPUs, or other reference research, which implies that the performance exposé of the articles are not comprehensive. Thus, this work lists the essential specifications of prior art edge AI accelerators and the CGRA accelerators during the past few years to define and evaluate the low power ultra-small edge AI accelerators. The actual performance, implementation, and productized examples of edge AI accelerators are released in this paper. We introduce the evaluation results showing the edge AI accelerator design trend about key performance metrics to guide designers. Last but not least, we give out the prospect of developing edge AI’s existing and future directions and trends, which will involve other technologies for future challenging constraints.

Original languageEnglish
Article number2048
JournalElectronics (Switzerland)
Volume10
Issue number17
Early online date25 Aug 2021
DOIs
Publication statusE-pub ahead of print - 25 Aug 2021

Keywords / Materials (for Non-textual outputs)

  • CGRA
  • CNN
  • Edge AI accelerator

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