01
The Essential Positioning of FPGA—Hardware Platform, Not a Conventional “Computer”
The greatest advantage of FPGA is its hardware-level flexible customization. It is particularly suitable for implementing high-speed parallel circuits for specific purposes, custom interfaces, timing control, and more. However, algorithm processing—often requiring flexible data access, complex control, massive arithmetic operations, and program flow—is an area where “general-purpose processors” (such as CPUs, DSPs, ARM) excel.
02
The “Mental Gap” Between Software Algorithms and FPGA
Software algorithm development essentially involves continuously writing code, modifying, and testing. This process is natural for CPUs/DSPs, which support floating-point operations, complex control flows, recursive calls, and other functions very efficiently.
FPGA, however, is completely different when it comes to algorithms; all steps must be “hard-coded” into the hardware. For example, an addition is not simply “write a line of code and it runs,” but rather “create the circuit for the adder,” and every data operation requires specific logical circuit support. As a result, the design is labor-intensive, maintenance is difficult, and making flexible adjustments is challenging.
03
Comparison of Development Difficulty and Cycle—”Building the Same Car, CPU is Like Assembling with Legos, FPGA is Like Forging Iron Parts”
On a CPU or DSP platform, the algorithm is like quickly assembling with Legos and can be easily modified.
On an FPGA, it’s like “melting steel—forging parts—then assembling the model,” with long cycles and difficult debugging.
A small change at the code level may require a major overhaul of hardware logic on FPGA.
04
Unfriendly Development Toolchain and Ecosystem
FPGA engineering development mainly relies on hardware description languages (such as Verilog, VHDL), which do not support the high-level languages commonly used by algorithm engineers for development and debugging. Many algorithms require mathematical libraries or ready-made algorithm support, which in the FPGA development environment often must be built from the ground up. This creates a high barrier to entry, poor flexibility, and low efficiency.
05
Efficiency Issues Due to Limited Resource Structure
Most of the resources in FPGA are used to construct various basic logic units and a small number of multipliers and storage units, rather than being designed for large-scale data algorithm processing.
The data interaction, complex arrays, special data structures, and frequent read/write operations involved in algorithms often require massive hardware resources to implement on FPGA, leading to wasted chip resources or even the inability to accommodate the design.
06
Architectural Differences in Data Flow and Program Flow
CPU/DSP architectures are suitable for complex program flows—such as conditionals, loops, function calls, and so on.
FPGA, on the other hand, is naturally suited for data-flow parallel processing—pipelining a large amount of data in one go, with high throughput and low latency (for example, in image capture, signal protocol processing, etc.).
Many algorithms are essentially “serial thinking,” while FPGA operates more like an “assembly line thinking,” and the two are incompatible.
07
Lack of Flexibility in Maintenance and Upgrades
For software algorithm updates, the CPU platform only requires reprogramming.
FPGA algorithms are “hard-wired” into the circuit, so every adjustment requires a complete redesign, re-layout, and timing analysis, resulting in extremely low development efficiency.
In summary: FPGA is suitable for creating “dedicated hardware accelerators” (such as high-parallel, deterministic latency data pathways and functional modules), but for flexible and complex algorithm processing, it’s like asking a production line to learn magic—not only inefficient but also difficult to maintain. Doing algorithms on FPGA is like building a large building with custom-made bricks for each one, while using a CPU for algorithms is like having a well-experienced renovation team—saving time and effort, with easy style changes.
Engineering Recommendation: Prioritize algorithm development on general-purpose platforms such as CPU/DSP.
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