Keynote 1: Dr. Hakaru Tamukoh
Associate Professor, Ph.D.
Department of Brain Science and Engineering, Graduate Schoolo of Life Science and Systems Engineering (LSSE), Kyushu Institute of Technology (Kyutech).
Hardware Accelerators for Brain-Like Artificial Intelligence on Home Service Robots
In recent years, service robots have become increasingly common. We develop home service robots Exi@ and Toyota HSR and attend RoboCup@Home that is an annual and the largest worldwide competition for home service robots. In this competition, robots perform practical applications in public and residential environments, e.g., acting as a waiter in a restaurant and tidy-up a children room. To realize such applications, robots require the implementation of intelligent processing, such as object recognition and detection, speech recognition, autonomous navigation and so on, and a significant number of intelligent processes run simultaneously. Therefore, the computing resources of embedded computers in a robot are always insufficient. In this talk, we briefly review the latest artificial intelligence for home service robots and our activities in RoboCup@Home competition. Then, we present a brain-inspired amygdala neural network model that learns preferences through human-robot interactions. Finally, we propose a “connective object for middleware to accelerator (COMTA),” which is a processing system that uses field programmable gate arrays (FPGAs) as hardware accelerators, and show examples of applications accelerated by COMTA for home service robots.
Keynote 2: Dr. Nattha Jindapetch
Assistant Professor, Ph.D.
Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Thailand
A Review of Design Methodologies for Heterogeneous FPGA-CPU Platforms
Recently, heterogeneous FPGA-CPU platforms are well suitable for adaptable and intelligent systems such as artificial intelligence (AI), 5G infrastructure, network interface card (NIC), software-defined networking (SDN), network function virtualization (NFV), data analytic, media processing, and advanced driver assistance systems (ADAS). The challenge is how developers bring their products to market quickly using existing design methodologies. For C/C++ software development, Xilinx offers SDAccel, SDSoC, and embedded development environments whereas Intel has HLS Compiler, SDK for OpenCL, SoC embedded development suite. For hardware development, Xilinx offers Vivado design suit whereas Intel has Quartus Prime. For system development, Xilinx offers System Generator for DSP whereas Intel has DSP Builder for Intel FPGAs. Moreover, Mathworks support model, verify, and program algorithms on FPGAs without writing any code. This talk will review these tools in more details.
Keynote 3: Dr. Bambang Sunaryo Suparjo
SoC Design Engineer, Ph.D.
Intel Corp. Hillsboro Oregon
Keynote 4: Dr Wan Zuha Wan Hasan
Associate Professor, Ph.D.
Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia, Malaysia
Real Time Self-Calibration Algorithm of Pressure Sensor for Robotic Hand Glove
It is about 15% of the world population have some form of disabilities which suffers for daily grasping activities due to cerebral vascular accident (CVA) or stroke. Proposing a wearable lightweight robotic hand glove that able to enhance the grasping mechanism and create a secure grasp. Since, existing glove was used only for normal grasping mechanism which no feedback response to assist the paralysed hands for securing the grasp. Thus, we introduce a wearable robotic hand gloves based on pressure sensors for a measurement system to generate a secure grasping through an adequate model with the least possible calibration time and highly accurate measurement data. However, as time elapses during manipulation in real time (RT), some pressure sensor parameters are changed due to hysteresis, variation in gain and lack of linearity. In fact, the calibration time and accurate performance of a pressure sensor are the most features to be processed in order to linearize the output signal sensors. In this case, a self-calibration algorithm based on artificial neural network is recommended to overcome the aforementioned issues. Therefore, A novel pressure sensor self-calibration estimation method with real time based on Artificial Neural Network (ANN) model. Our approach provides a real time self-calibration method of wearable robotic hand glove to obtain and calibrate the model for pressure estimation to obtain an accurate pressure for the grasped object, the secure grasp as well as rehabilitation system for patient with paralysed hand can be successfully designed and achieved.