FPGA Technology for Compute‑Intensive and Communication‑Intensive Tasks in Data Centers

FPGA can handle compute‑intensive tasks by leveraging its pipeline parallel structure, offering lower result‑return latency compared to CPU and GPU. Typical compute‑intensive workloads include matrix calculations, machine vision, image processing, search‑engine ranking, and asymmetric encryption, which can be offloaded from CPU to FPGA.

Performance vs. CPU: A Stratix FPGA performs integer multiplication comparable to a 20‑core CPU and floating‑point multiplication comparable to an 8‑core CPU.

Performance vs. GPU: FPGA integer and floating‑point multiplication lag behind GPU by orders of magnitude, but can approach GPU performance by configuring multipliers and floating‑point units.

Core advantages for latency‑critical tasks: FPGA reduces PCIe latency to microseconds and, with future advances, can achieve sub‑100‑nanosecond CPU‑FPGA communication.

FPGA’s pipeline parallelism enables instant output after processing multiple pipeline stages, giving it a natural edge over GPU’s data‑parallel model for streaming workloads.

Communication‑intensive tasks: Symmetric encryption, firewalls, and network virtualization benefit from FPGA’s ability to process packets at line speed (40‑Gbps/100‑Gbps) without NIC bottlenecks, delivering throughput and stable low latency.

Throughput advantage: CPU packet processing is limited by NIC speed and PCIe slots; FPGA can directly handle line‑rate traffic, lowering network‑card and switch costs.

Latency advantage: CPU‑based DPDK processing incurs ~5 µs latency, potentially rising to tens of µs under load, whereas FPGA provides stable sub‑µs latency without instruction overhead.

FPGA deployment models include cluster, distributed, and shared‑server network configurations, each with distinct characteristics and constraints such as inter‑FPGA communication limits, fault propagation, and operational costs.

Cluster deployment packs multiple FPGA cards into a single server, but lacks cross‑machine communication and suffers from single‑point failures.

Distributed deployment embeds FPGA in each server and connects them via dedicated networks, improving fault tolerance and reducing latency.

Shared‑server network deployment places FPGA between NICs and switches, enabling virtual NICs for VMs, boosting virtual network performance (e.g., 25 Gbps) and reducing latency by up to tenfold.

eFPGA technology offers superior performance, cost, power, and profitability compared to traditional FPGA‑ASIC integration, with re‑programmability supporting rapid market adaptation.

Power‑efficient eFPGA designs can improve total system performance while lowering overall power consumption.

In cloud data centers, FPGA accelerates compute‑intensive tasks via OpenCL‑based high‑level programming, with data flowing CPU → DRAM → FPGA → DRAM → CPU. Current DRAM‑mediated communication adds ~2 ms latency, but PCIe DMA can reduce it to ~1 µs.

Future scheduling models may reverse the traditional CPU‑to‑FPGA offload, allowing FPGA‑centric processing with CPU handling fragmented tasks.

The global FPGA market is dominated by Xilinx, Intel (Altera), Lattice, and Microsemi, holding over 9,000 patents. Chinese vendors such as Unigroup, Guowei, Chengdu Huamei, Anlu, Zhiduocheng, Gaoyun, Shanghai Fudan, and Jingwei are developing high‑gate‑count FPGA products and targeting AI, autonomous driving, and other emerging markets.

Chinese FPGA companies face challenges in unified design and application software, presenting an opportunity for leading vendors to consolidate the ecosystem and enhance competitiveness.