Deterministic Java in tiny embedded systems

As embedded systems become more and more complex, and the time to market becomes shorter, there is a need in the embedded systems community to find better programming languages that let the programmers develop correct code faster. The programming languages used today, typically C and/or Assemblers, are just too error-prone. The Java technology has therefore gained a lot of interest from developers of embedded systems in the last few years. We propose an approach based on compiling Java into native machine code via C as an intermediate language. The C code generation process should also add close interaction with a fully pre-emptive incremental garbage collector and a small and efficient real time kernel. Tests performed on a small 8-bit microprocessor show that it is possible to use a modern object oriented language with automatic memory management, such as Java, and yet generate fully predictable code that can be run in very small devices with severe memory constraints

Evaluating Rapid Application Development with Python for Heterogeneous Processor-based FPGAs

As modern FPGAs evolve to include more het- erogeneous processing elements, such as ARM cores, it makes sense to consider these devices as processors first and FPGA accelerators second. As such, the conventional FPGA develop- ment environment must also adapt to support more software- like programming functionality. While high-level synthesis tools can help reduce FPGA development time, there still remains a large expertise gap in order to realize highly performing implementations. At a system-level the skill set necessary to integrate multiple custom IP hardware cores, interconnects, memory interfaces, and now heterogeneous processing elements is complex. Rather than drive FPGA development from the hardware up, we consider the impact of leveraging Python to ac- celerate application development. Python offers highly optimized libraries from an incredibly large developer community, yet is limited to the performance of the hardware system. In this work we evaluate the impact of using PYNQ, a Python development environment for application development on the Xilinx Zynq devices, the performance implications, and bottlenecks associated with it. We compare our results against existing C-based and hand-coded implementations to better understand if Python can be the glue that binds together software and hardware developers.Comment: To appear in 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM'17