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VeBPF Many-Core Architecture for Network Functions in FPGA-based SmartNICs and IoT

Published: December 14, 2025 | arXiv ID: 2512.12778v1

By: Zaid Tahir , Ahmed Sanaullah , Sahan Bandara and more

Potential Business Impact:

Makes computer networks process data much faster.

Business Areas:
Field-Programmable Gate Array (FPGA) Hardware

FPGA-based SmartNICs and IoT devices integrating soft-processors for network function execution have emerged to address the limited hardware reconfigurability of DPUs and MCUs. However, existing FPGA-based solutions lack a highly configurable many-core architecture specialized for network packet processing. This work presents VeBPF many-core architecture, a resource-optimized and highly configurable many-core architecture composed of custom VeBPF (Verilog eBPF) CPU cores designed for FPGA-based packet processing. The VeBPF cores are eBPF ISA compliant and implemented in Verilog HDL for seamless integration with existing FPGA IP blocks and subsystems. The proposed many-core architecture enables parallel execution of multiple eBPF rules across multiple VeBPF cores, achieving low-latency packet processing. The architecture is fully parameterizable, allowing the number of VeBPF cores and eBPF rules to scale according to application requirements and available FPGA resources. eBPF rules can be dynamically updated at run time without requiring FPGA reconfiguration, enabling flexible and adaptive network processing. The design incorporates hardware and computer architecture optimizations that support deployment across a wide range of platforms, from low-end FPGA-based IoT devices to high-end FPGA-based SmartNICs. In addition, we present automated testing and simulation frameworks developed using open-source tools such as Python and Cocotb. The VeBPF cores, many-core architecture, control software libraries, and simulation infrastructure are released as open source to support further research in FPGA-based many-core systems, eBPF acceleration, SmartNICs, IoT, and network security.

Country of Origin
🇺🇸 United States

Repos / Data Links

Page Count
9 pages

Category
Computer Science:
Computational Engineering, Finance, and Science