As machine vision systems push toward higher resolutions and faster frame rates, the increase in image data volume is pushing the limits, and in some cases, outpacing the capabilities of traditional network configurations. As integrators continue to adopt more and more 10GigE and 25GigE cameras with high-throughput and complex vision processing, they may encounter packet loss which can lead to increased CPU load and inconsistent latency. Addressing these challenges often requires careful optimization or, in some cases, custom or proprietary solutions. But with the introduction of GigE Vision 3.0 and support for RDMA via the RoCE v2 standard, the industry is adopting a high-performance transport protocol that has already been proven in data centers and HPC (high-performance computing) environments. This article explores how the two standards, GigE Vision and RoCE v2, are converging to deliver more efficient, scalable, and future-proof machine vision networks.

The GigE Vision standard was introduced in 2006 with the goal to unify how image data is transmitted over Ethernet in machine vision applications. It established a common framework based on UDP to ensure interoperability between cameras, software, and networking hardware. Over time, GigE Vision added features like packet retransmission and chunk data (GigE Vision 1.1, 2008), official support for link aggregation and 10 Gigabit, via IEEE 802.3ba, compression support, and non-streaming device control (GigE Vision 2.0, 2012), buffer and event handling improvements, more complex 3D data structure support (GigE Vision 2.1, 2018), and GenDC streaming and multi event data support (GigE Vision 2.2, 2022).

In parallel, the data center and HPC industries were solving a different problem: how to move large volumes of data between servers with minimal latency and CPU load. This led to the development of RDMA technologies and the introduction of RoCE (RDMA over Converged Ethernet). RoCE was developed by the InfiniBand Trade Association (IBTA) as a way to extend InfiniBand’s high-performance RDMA transport over standard Ethernet networks. RoCE v1 (2010) operated at Layer 2 and was limited to local network segments, but RoCE v2 (2014) expanded this by adding UDP/IP encapsulation, making it routable and scalable across standard Ethernet networks. Over the past decade, even though competing RDMA technologies existed, RoCE v2 matured and became widely adopted in cloud computing, storage, and real-time analytics.

However, as time progressed, the demand for multi-10GigE camera applications grew to a level where RDMA had to be reassessed. More and more customers turned to Ethernet not just for speed, but also for its industrial reliability and network flexibility. Features like built-in electrical isolation, long cable lengths, Power over Ethernet (PoE), Precision Time Protocol (PTP), and action commands made Ethernet ideal for complex, scalable vision systems. Users wanted the industrial benefits of Ethernet with the bandwidth of 10GigE and beyond. However, as they began deploying multi-10GigE camera systems and implementing more advanced vision processing, many encountered high CPU load and reliability issues caused by packet loss and retransmissions over UDP. The increase in CPU resources needed to manage multiple 10GigE camera data streams made it clear that the time had come once again to re-evaluate RDMA for the GigE Vision standard.

Meanwhile, the situation also evolved for RoCE v2 RDMA. Thanks to its UDP encapsulation, adoption of RoCE v2 grew exponentially. With the release of Broadcom’s BCM57414 and BCM957414 chipsets, RDMA NICs also became more accessible and affordable, with NIC prices matching non-RDMA NICs. Operating system support had matured on both Windows and Linux, and the RoCE v2 protocol became well understood and widely deployed, beating out competing RDMA technologies. Because of these reasons, the GigE Vision standards committee moved forward with integrating RoCE v2 RDMA into the specification as an optional transport protocol. While the original UDP transport protocol remains supported, RDMA offers a standards-compliant alternative for systems requiring higher bandwidth needs without utilizing CPU resources for managing the data streams.

To implement RDMA communication, developers use low-level programming APIs known as the verbs. On Linux, the libibverbs library provides access to these verbs in user space. On Windows, Microsoft offers the Network Direct Service Provider Interface. These libraries give software and camera manufacturers access to the RDMA hardware features required for high-performance streaming. For example, they allow RDMA streaming support to be integrated into LUCID’s Arena SDK, in ArenaView GUI and APIs, as well as other GigE Vision compliant software.

The integration of RDMA via RoCE v2 into the GigE Vision 3.0 standard marks a significant advancement for high-performance machine vision systems. While the technical foundations for RDMA have existed for years, only recently has the surrounding ecosystem matured enough to make this technology practical and cost-effective in industrial imaging environments.

For users, the value lies not just in performance, but in standardization. GigE Vision 3.0 defines how RDMA should be used across devices and platforms, while RoCE v2 provides a reliable, IP-routable transport that has already been proven at scale in data centers, cloud infrastructure, and HPC systems. This means the protocols involved are no longer experimental or proprietary. They are well-defined, widely implemented, and thoroughly validated under demanding real-world conditions. With the addition of RDMA into the GigE Vision standard, users are assured that hardware and software from different vendors will work together reliably, reducing integration time and cost. It also ensures long-term support, ease of scalability, and a wider range of compatible tools and components.