A Low-power Programmable Machine Learning Hardware Accelerator Design for Intelligent Edge Devices

Abstract

With the advent of the machine learning and IoT, many low-power edge devices, such as wearable devices with various sensors, are used for machine learning–based intelligent applications, such as healthcare or motion recognition. While these applications are becoming more complex to provide high-quality services, the performance of conventional low-power edge devices with extremely limited hardware resources is insufficient to support the emerging intelligent applications. We designed a hardware accelerator, called an Intelligence Boost Engine (IBE), for low-power smart edge devices to enable the real-time processing of emerging intelligent applications with energy efficiency and limited programmability. The measurement results confirm that the proposed IBE can reduce the power consumption of the edge node device by 75% and achieve performance improvement in processing the kernel operations of applications such as motion recognition by 69.9 times.