Cpus and Gpus are good at controlling flow computing. Their control-driven machine model is based on control tokens that indicate when statements should be executed. This gives the CPU and GPU complete control and can easily implement complex data and control structures. However, this comes at the cost of being less efficient and difficult to program accurately without error. Fpgas, on the other hand, excel at data flow arithmetic. They follow the pattern of data-driven machines that execute statements as soon as all operands are available. The result is that FPGas unlock huge parallelism and throughput potential without errors or side effects.
In general, as an alternative technology for CPU and GPU common platform, FPGA can adaptively generate custom computing architecture to meet the needs of robots. Because of their unprecedented flexibility and ability to shorten design cycles while reducing development costs, FPGas have been widely adopted in a wide range of well-known industrial robot manufacturers and medical robot applications. In "A Survey of FPGA-based roboTIc compuTIng," you can see A survey of FPGA-based roboTIc compuTIng that demonstrates the wide applicability of FPGas to roboTIc applications. The following is a detailed description of FPGA features:
• Adaptive: When both control flow and data flow are needed, the CPU and GPU are unusable due to delay and response time issues, while fpGas generate an unmatched custom computing architecture that meets stringent real-time requirements and multiple critical requirements. The fixed computing architecture of cpus and Gpus limits their overall capabilities, including response time and latency.
• High performance: FPgas improve computing performance by establishing deeply pipelined data paths (stream computing), rather than relying on an increase in the number of computing units as cpus and Gpus do. The working principle of stream computing is that the data generated by one computing unit is immediately processed by the next computing unit in the pipeline. In this way, the "fetch-compute-store" link in the data flow channel is exempted, which is convenient for data producers and consumers to operate and thus improves the performance. On the contrary, cpus and Gpus can only perform calculations at the expense of performance due to multiple constraints such as fixed architecture, fixed number of cores, fixed instruction set, and rigid memory architecture.
• High energy efficiency: Speed and power consumption are basic figures of merit for digital circuits. Power is a function of digital circuit frequency and trigger rate. The FPGA adjusts the frequency through parallel and direct execution algorithms. Fpgas maintain lower frequencies and lower switching rates (no instruction acquisition) for computation, but FPGas have greater parallelism advantages at higher frequencies compared to equivalent computing performance of CPU and GPU, enabling customers to achieve better power index and higher energy efficiency.
• No wasted computing power: FPGas maximize chip utilization with flexibility to improve performance. Dynamic functional switching (DFX, formerly known as "partial reconfiguration") allows threaded applications running on cpus to share time with FPGas. Thus, while a given thread is processing a result generated by the FPGA, another thread can use the FPGA to perform a different calculation.
• Predictable: FPGas help cpus and Gpus offload strictly real-time computing, provide nanosecond predictive capabilities in execution times, and are not affected by software changes or jitter related to GPU and CPU computing.
• Reconfigurable: Robot algorithms are still evolving at a high rate, and FPGas can dynamically reconfigure and update on demand. In addition, fpGas can be easily reprogrammed to meet heterogeneous requirements and achieve common capabilities that only CPUS and Gpus can provide.
• Security: FPGas can flexibly build security circuits on demand to ensure the security of robot data flow. In addition, FPGas can take full advantage of reconfiguration to correct defects in their hardware architecture (avoiding hardware risks). This allows designers to quickly address security risks (avoiding future risks such as "meltdowns" and "ghosts") that are difficult or impossible to resolve on a fixed computing architecture.
However, it is also argued that while FPgas are the ideal computing backbone for roboticists, the flexibility they offer comes at the cost of increased complexity and required design skills. "A Survey of FPGA-based roboTIc compuTIng" lists some of the additional skills required. The best robot performance can be achieved only when all these technologies, including multi-core CPU, GPU and FPGA, can be used comprehensively and comprehensively. In fact, the integrated system-on-chip (SoC) solution provided by Celins is a perfect combination of the programmable capabilities of CPU general software and the adaptive hardware functions of FPGA in the same device.
These adaptive soCs provide both hardware and software for highly flexible computing foundations for robotics applications and provide high performance, low power consumption, deterministic, reconfigurable hardware, security, and adaptive characteristics.
Summary: CPU and GPU are good at control flow calculation, while FPGA is good at data flow calculation. Adaptive SoC solutions provide a highly flexible computing base with both hardware and software for robotics applications, providing low power consumption, high performance, deterministic, reconfigurable hardware, security, and adaptive features.
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