In recent days, AlphaGo, which has long since retired, has made headlines again—not by competing with top human players, but by defeating its own previous versions through unsupervised learning. In just three days, it overcame the Li Shishi version of AlphaGo, and within 21 days, it defeated the Kejie version. This performance truly highlights the immense power of AI computing.
Currently, the two most widely used acceleration components in AI computing platforms are GPUs and FPGAs. GPUs excel in deep learning training, where high parallelism and SIMD (Single Instruction Multiple Data) operations are essential. They are ideal for creating CNN, DNN, RNN, LSTM, and other enhanced learning networks. However, when it comes to inference—especially with small batch data—GPUs struggle to fully utilize their parallel computing capabilities.
FPGAs, on the other hand, offer a different set of advantages. They support both pipeline and data parallelism, resulting in lower latency for task processing. For example, if a data packet requires 10 steps to process, an FPGA can create a 10-stage pipeline, allowing different data packets to be processed simultaneously at each stage. This leads to faster throughput and lower latency, often under a microsecond. With Intel’s Xeon + FPGA setup using QPI interconnects, latency can even drop below 100 nanoseconds.
Another key advantage of FPGAs is their programmability. Since they are software-defined hardware, they allow for flexible algorithm updates. As deep learning algorithms continue to evolve, this adaptability ensures that FPGAs can quickly adjust to new requirements, making them ideal for fast-paced AI development.
Looking ahead, more than 95% of machine learning tasks will be inference-based, and FPGAs are particularly well-suited for this. They not only improve inference efficiency but also maintain high accuracy, making them a strong choice for real-time applications.
But what about performance? Unlike CPUs and GPUs, FPGAs use a non-Von Neumann architecture, adapting hardware to software needs. This allows for optimized parallelism, leading to higher energy efficiency than traditional processors.
However, one concern remains: the development cycle. Traditional FPGA development using HDL languages can take up to a year, which may not align with the fast-paced nature of internet businesses. To address this, Inspur introduced OpenCL-based development, reducing the cycle from 12 months to less than 4 months. This makes FPGA deployment more agile and accessible.
For companies lacking technical resources, deploying FPGA solutions can still be challenging. That’s where FaaS (FPGA as a Service) comes into play. By integrating software, algorithms, and hardware, FaaS provides ready-to-use services that significantly reduce the entry barrier.
Inspur has already developed several application-specific solutions, including neural network acceleration, image compression, and data compression. For instance, the F10A AI inference solution achieves a classification speed of 742 images per second with 99.6% Top-5 accuracy, offering three times the energy efficiency of a comparable GPU.
In webp transcoding, the FPGA-based solution improves processing speed by up to 14 times compared to CPU, while the GZip compression solution boosts throughput by 10 times. These results demonstrate the potential of FPGAs in accelerating data-intensive tasks.
FPGA management and cloud integration are also becoming more user-friendly. FaaS supports remote reconfiguration, dynamic logic updates, and virtual machine access. It includes RAS features like dual loading modes, ensuring high reliability and availability.
Finally, many companies are hesitant to adopt new technologies like FPGAs. But with growing adoption by major tech firms such as Baidu, Alibaba, Tencent, NetEase, and Keda Xunfei, the benefits are becoming clear.
FPGAs are finding applications in machine learning, finance, big data, gene detection, network security, image processing, natural language processing, and speech recognition. As FPGAs become more accessible as a cloud-based computing service, the barriers to entry are being lowered.
In the future, the combination of CPUs and FPGAs will likely become a standard in heterogeneous computing, opening new possibilities across industries. So, why wait? The future of AI is here—and it's powered by FPGAs.
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