Why FPGAs Are Faster Than CPUs and GPUs

Why FPGAs Are Faster Than CPUs and GPUs
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Both CPUs and GPUs belong to the von Neumann architecture, which involves instruction decoding and execution with shared memory. The reason FPGAs are faster than CPUs and GPUs is fundamentally due to their architecture, which lacks instructions and shared memory.
In the von Neumann structure, since execution units can execute any instruction, there is a need for instruction memory, decoders, various instruction arithmetic units, and branch jump processing logic. Each logic unit in an FPGA has its functionality determined during reprogramming, eliminating the need for instructions.
In the von Neumann architecture, memory serves two purposes: 1) to save state; 2) to facilitate communication between execution units.
1) Saving state: Registers and on-chip memory (BRAM) in FPGAs belong to their respective control logic, eliminating unnecessary arbitration and caching.
2) Communication needs: The connections between each logic unit in an FPGA are determined during reprogramming and do not require communication via shared memory.
In compute-intensive tasks:
In data centers, the core advantage of FPGAs over GPUs lies in latency. Why is FPGA latency much lower than that of GPUs? It is fundamentally due to architectural differences. FPGAs possess both pipeline parallelism and data parallelism, whereas GPUs predominantly have data parallelism (pipeline depth is limited).
Processing a data packet involves 10 steps; FPGAs can build a 10-stage pipeline where different stages handle different packets, completing processing after 10 stages. Each processed packet can be output immediately. In contrast, GPUs use data parallelism with 10 computing units, each handling different packets, but all units must operate in unison, performing the same tasks (SIMD). This requires that 10 packets must enter and exit simultaneously. When tasks arrive individually rather than in batches, pipeline parallelism achieves lower latency than data parallelism. Thus, for pipeline-computing tasks, FPGAs inherently have a latency advantage over GPUs.
ASICs excel in throughput, latency, and power consumption individually. However, their R&D costs are high, and development cycles are long. The flexibility of FPGAs can protect assets. Data centers are rented to different tenants. Some machines have neural network acceleration cards, some have Bing search acceleration cards, and others have network virtualization acceleration cards, making task scheduling and operations cumbersome. Using FPGAs can maintain homogeneity in data centers.
In communication-intensive tasks, the advantages of FPGAs over GPUs and CPUs are even more pronounced.
1) Throughput: FPGAs can directly connect to 40Gbps or 100Gbps network cables, processing data packets of any size at line speed; whereas CPUs require network cards to receive packets. GPUs can also process packets efficiently, but they lack network ports and also rely on network cards, which limits throughput based on the network card and/or CPU.
2) Latency: Network cards pass data to CPUs, which then send it back to the network cards, adding instability to latency due to clock interrupts and task scheduling in the system.
In summary, the main advantages of FPGAs in data centers are their stability and extremely low latency, making them suitable for both compute-intensive and communication-intensive tasks.
The biggest difference between FPGAs and GPUs lies in their architecture; FPGAs are better suited for low-latency streaming processing, while GPUs are more suitable for processing large batches of homogeneous data.
Success and failure both stem from the same source. The lack of instructions is both an advantage and a weakness of FPGAs. Each different task requires a certain amount of FPGA logic resources. If the tasks are complex and not highly repetitive, they will occupy a large amount of logic resources, most of which will remain idle. In such cases, using a von Neumann structure processor is more advantageous.
FPGAs and CPUs work together, with locality and high repetition assigned to FPGAs and complexity assigned to CPUs.

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Why FPGAs Are Faster Than CPUs and GPUs
Why FPGAs Are Faster Than CPUs and GPUs

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