Low-power ultra-small Edge AI Accelerators for Image Recognition with Convolution Neural Networks: Analysis and Future Directions

Abstract

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 not only require meeting performance targets but also meeting strict reliability and resilience constraints due to operations in harsh and hostile environments. Numerous research articles have been proposed, but not all of these include full specifications. Most of these tend to compare their architecture with other existing CPUs, GPUs, or other reference research. This implies that the performance results of the articles are not comprehensive. Thus, this work lists the three key features in the specifications such as computation ability, power consumption, and the area size 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. We introduce the actual evaluation results showing the trend in edge AI accelerator design about key performance metrics to guide designers on the actual performance of existing edge AI accelerators’ capability and provide future design directions and trends for other applications with challenging constraints.

Computational Failure Analysis of In-Memory RRAM Architecture for Pattern Classification CNN Circuits

“…Battery-powered IoT applications in the real-world demand low operation power for a long lifetime; hence compromising prediction accuracy by choosing smaller current compliance will significantly extend battery life. Recent studies show edge AI applications with fault tolerance are the next trend for designing low-powered IoT edge devices in monitoring and sensing in remote applications such as oil platforms, covered drain, remote surveillance systems, etc... [36]. An overall prediction error tolerance of 20 to 40% is acceptable in such applications, where sampling and regression trend analysis can determine the error deviation.…”