The Future of AI Servers
Following up on the recent announcement of our new Raspberry Pi 4 AI Servers, it seems that AI servers running on Arm processors are gaining more and more traction in the market due to their natural fit at the IoT and Edge layers of infrastructure. Let’s take a quick look at some of the unique properties that make AI Servers running on Arm a great strategy for AI/ML and AIoT deployments, to help understand why this is so important for the future.
Power – Many IoT deployments do not have luxuries that “regular” servers enjoy such as reliable power and connectivity, or even ample power for that matter. While Intel has spent decades making excellent, though power hungry processors, Arm has focused on efficiency and battery life, helping to explain they they dominate the market in tablets and smartphones. This same efficiency is then leveraged by IoT devices running AI workloads, so Edge devices responsible for computer vision, image classification, object detection, deep learning, or other workloads can operate with a much lower thermal footprint than a comparable x86 device.
Size – Similar to the underlying reasons behind power efficiency, the physical size and dimensions of Arm AI Servers can be made smaller than the majority of x86 designs. Attaching AI Accelerators such as the Gyrfalcon 2801 or 2803 via USB to boards as small as 2 inches square (such as the NanoPi Neo2) is possible, or the addition of a Google Coral TPU via the mini-PCIe slot on a NanoPi T4 bring an enormous amount of inferencing to AI Servers in tiny form factors.
Cost – Here again, Arm SoC’s and Single Board Computers typically have a rather large cost advantage versus x86 embedded designs.
Scalability – This is a critical factor in why Arm will play a massive role in the future of AI Servers, and why miniNodes has begun to offer our Raspberry Pi 4 AI Server. As mentioned, low power, cheap devices make great endpoints, but, there is also a role for “medium” sized AI servers handling larger workloads, and Arm partners are just now starting to bring these products to market. An example is the SolidRun Janux AI Server, which also makes use of the same Gyrfalcon AI Accelerators used by our nodes. So, you can get started training your models, testing out your deployment pipeline, understanding the various AI frameworks and algorithms, and getting comfortable with the tools, and very easily scale up as your needs expand. Of course, once you reach massive amounts of deep learning and AI/ML processing, enterprise Arm server options exist for that as well.
Flexibility – Taking Scalability one step further, the Arm AI servers also allow for a great amount of flexibility in the specific accelerator hardware (Gyrfalcon, Google Coral, Intel Movidius), the frameworks used (Caffe, PyTorch, TensorFlow, TinyML), and the models (ResNet, MobileNet, ShuffleNet) employed.
Ubiquity – A final piece of the overall AI Server ecosystem is the ease of access to this type of hardware, and low barriers to entry. The Raspberry Pi and similar types of boards are distributed globally, and readily available in nearly all markets.
As you can see, our view is that the future of AI servers is based on Arm SoC’s, and now is the time to start exploring what that means for your workload.