Abstract-This paper aims to simplify FPGA designs that incorporate Embedded Software Systems using a soft core Processor. It describes a simple solution to reduce the need of multiple non-volatile memory devices by using one SPI (Serial Peripheral Interface) Flash PROM for FPGA configuration data, software code (Processor applications), and miscellaneous user data. We have thus developed a design based on a MicroBlaze soft processor implemented on a Xilinx Spartan-6 FPGA SP605 Evaluation Kit. The hardware architecture with SPI flash was designed using the Xilinx Platform Studio (XPS) and the software applications, including the bootloader, was developed with Xilinx Software Development Kit (SDK). ISE Design Tools prepared by Xilinx Company, is employed to create the files used to program flash memory which are SREC (S-record) file associated with software code, Hexadecimal file for user data, and bootloader file to configure the FPGA and allows software applications stored in flash memory to be executed when the system is powered on. Reading access to the SPI Flash memory is simplified by the use of Xilinx In-System Flash (ISF) library.

Abstract-this paper aims to contribute to the efforts of design community to demonstrate the effectiveness of the state of the art Field Programmable Gate Array (FPGA), in the embedded systems development, taking a case study in the biomedical field. With this design approach, we have developed a System on Chip (SoC) for cardiac monitoring based on the soft processor MicroBlaze and the Xilkernel Real Time Operating System (RTOS), both from Xilinx. The system permits the acquisition and the digitizing of the Electrocardiogram (ECG) analog signal, displaying heart rate on seven segments module and ECG on Video Graphics Adapter (VGA) screen, tracing the heart rate variability (HRV) tachogram, and communication with a Personal Computer (PC) via the serial port. We have used the MIT_BIH Database records to test and evaluate our implementation performance. In terms of the resources utilization, the implementation occupies around 70% of the used FPGA, namely the Xilinx Spartan 6 XC6SLX16. The accuracy of the QRS detection exceeds 96%.

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