The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

Recently, IoT applications using Deep Neural Network (DNN) to embedded edge devices are increasing. Generally, in the case of DNN applications in the IoT system, training is mainly performed in the server and inference operation is performed on the edge device. The embedded edge devices still take a lot of loads in inference operations due to low computing resources, so proper customization of DNN with architectural exploration is required. However, there are few integrated frameworks to facilitate exploration and customization of various DNN models and their operations in embedded edge devices. In this paper, we propose an integrated framework that can explore and customize DNN inference operations of DNN models on embedded edge devices. The framework consists of the GUI interface part, the inference engine part, and the hardware Deep Learning Accelerator (DLA) Virtual Platform (VP) part. Specifically it focuses on Convolutional Neural Network (CNN), and provides integrated interoperability for convolutional neural network models and neural network customization techniques such as quantization and cross-inference functions. In addition, performance estimation is possible by providing hardware DLA VP for embedded edge devices. Those features are provided as web-based GUI interfaces, so users can easily utilize them.

Program optimization or software optimization is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources. When developing software for embedded systems, open source libraries are usually used. An Embedded software built using a variety of open software and libraries is apt to have too many unused codes because open source libraries contain many functions and features to be used in various applications. In this paper, we describe the process of a library optimization method to reduce memory consumption for the user interface software of a set-top box product. The overall optimization process uses freeware tools developed for the Linux operating system. We devised an optimization technique to compensate for operation imperfections in the target system. The original Qt library of 19.57 MB was optimized to be 7.26 MB in program image size. In the case of the DirectFB library, 3.2 MB was reduced to 2.4 MB. The optimization process can be applied to any embedded software that are developed with open source libraries or sources.

Interactive learning is becoming important in teaching and learning. In particular, the electronic board has educational significance in terms of being able to share and discuss learner's contents with instructors. However, the existing electronic board is not easily used in an educational environment due to the high cost and complicated installation process. This paper propose a mathematical model to calibrate the position of user coordinates by installing two laser sensors and a reflector on a wall. We virtually locate 9 points with software. To validate the algorithm, Raspberry Pi2 is used as a virtual sensor and simulation was performed by PC using RS-232 communication. This algorithm is powerful because up to four pre-saved coordinates are used to provide the higher accuracy.