A review of CNN accelerators for embedded systems based on RISC-V

Abstract

One of the great challenges of computing today is sustainable energy consumption. In the deployment of edge computing this challenge is particularly important considering the use of embedded equipment with limited energy and computation resources. In those systems, the energy consumption must be carefully managed to operate for long periods. Specifically, for embedded systems with machine learning capabilities in the Internet of Things (EMLIoT) era, the convolutional neural networks (CNN) model execution is energy challenging and requires massive data. Nowadays, high workload processing is designed separately into a host processor in charge of generic functions and an accelerator dedicated to executing the specific task. Open-hardware-based designs are pushing for new levels of energy efficiency. For achieving energy efficiency, open-source tools, such as the RISC-V ISA, have been introduced to optimize every internal stage of the system. This document aims to compare the EMLIoT accelerator designs based on RISC-V and highlights open topics for research.

Analyzing and Comparing Deep Learning Models on an ARM 32 Bits Microcontroller for Pre-Impact Fall Detection

“…Dense model was chosen as it is the base layer characterized by interlayer connections between adjacent layers and performing linear transformations on input data for the next layer, mainly for the output layer. The CNN model [51], [52] is commonly used for computer vision tasks such as object detection, image segmentation, video recognition [53] or optimization energy consumption for embedded systems [54]. The LSTM model is suitable to classification and prediction of future values based on time series data in anomaly detection, stock prediction or gesture classification [55].…”

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

“…Recently, especially after the COVID-19 epidemic, the open RISC-V ISA has gained enormous popularity in the avionic power electronics and fault tolerance domain [71]. For achieving energy efficiency, open-source tools, such as the RISC-V ISA, have been introduced to optimize every internal stage of the system [72]. Figure 8 illustrates the similarity of the development cycle between RISC-V Vector operation and AI operation, which leads to the possibilities for ML acceleration.…”