FPGAs are compelling platforms for hardware acceleration of embedded systems. These devices, by virtue of their massively parallel structures, provide embedded systems designers with new alternatives for creating high-performance applications.
There are challenges to using FPGAs as software platforms, however. Historically, low-level hardware descriptions must be written in VHDL or Verilog, languages that are not generally part of a software programmer's expertise. Other challenges have included deciding how and when to partition complex applications between hardware and software and how to structure an application to take maximum advantage of hardware parallelism.
Tools providing C compilation and optimization for FPGAs can help solve these problems by providing a new level of programming abstraction. When FPGAs first appeared two decades ago, the primary method of design for these devices was the venerable schematic. FPGA application developers used schematics to assemble low-level components (registers, logic gates, and larger blocks such as counters and adders/subtractors) to create FPGA-based systems. As FPGA devices became more complex and applications targeting them grew larger, schematics were gradually replaced by higher level methods involving hardware description languages like VHDL and Verilog. Now, with ever-higher FPGA gate densities and the proliferation of FPGA embedded processors, there is strong demand for even higher levels of abstraction. C represents that next generation of abstraction, allowing you to access the resources of FPGAs for application acceleration.
For applications that involve embedded processors, a C-to-hardware tool such as Impulse C (Figure 1) can abstract away many of the details of hardware-to-software communication, allowing you to focus on application partitioning without having to worry about the low-level details of the hardware. This also allows you to experiment with alternative software/hardware implementations.
Figure 1. Impulse C custom hardware accelerators run in the FPGA fabric to accelerate µClinux processor-based applications.
Although such tools can dramatically improve your ability to create FPGA-based applications, for the highest performance you still need to understand certain aspects of the underlying hardware. In particular, you must understand how partitioning decisions and C coding styles will impact performance, size, and power usage. For example, the acceleration of critical computations and inner-code loops must be balanced against the expense of moving data between hardware and software. Fortunately, modern tools for FPGA compilation provide various types of analysis tools that can help you more clearly understand and respond to these issues.
Practically speaking, the initial results of software-to-hardware compilation from C-language descriptions will not equal the performance of hand-coded VHDL, but the turnaround time to get those first results working may be an order of magnitude better. Performance improvements occur iteratively, through an analysis of how the application is being compiled to the hardware and through the experimentation that C-language programming allows.
Graphical tools (see Figure 2) can help to provide initial estimates of algorithm throughput such as loop latencies and pipeline effective rates. Using such tools, you can interactively change optimization options or iteratively modify and recompile C code to obtain higher performance. Such design iterations may take only a matter of minutes when using C, whereas the same iterations may require hours of even days when using VHDL or Verilog.
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Figure 2. A dataflow graph allows C programmers to analyze the generated hardware and perform explorative optimizations to balance tradeoffs between size and speed. Illustrated in this graph is the final stage of a six-stage pipelined loop. This graph also helps C programmers understand how sequential C statements are parallelized and optimized.