(untitled) (Revista de Investigación Intercultural ASAMPITAKOYETE, 2025)
Edge AI integrates AI, edge computing, and mobile edge computing to process data near the source with low latency, low power consumption, and greater privacy. It addresses the need to run local inference on resource-constrained devices (sensors, MCUs, SBCs like the Jetson Nano or Raspberry Pi, FPGAs, and accelerators like the Edge TPU) without relying on the cloud for real-time processing. This systematic review focuses on hardware-software co-optimization: compact models (quantized and pruned CNNs, lightweight YOLO-lite variants, SNNs) and toolchains (TensorFlow Lite Micro, CMSIS-NN, TVM/microTVM) that balance accuracy, latency, and power while minimizing RAM/Flash footprint. This approach is key in healthcare, transportation, industrial security, and home automation. Although there is a history of this field, it has grown rapidly in the last five years. Articles published between 2020 and 2025 were reviewed using the Google Scholar and Scopus search engines, with Boolean operators applied. Twenty-four articles were included based on computational models, edge hardware, and energy metrics. Edge AI in embedded systems has matured in the development process, but it still relies heavily on prototype development platforms. Similarly, energy metrics need to be standardized to compare realistic performance between solutions, considering external factors that may affect them.