GPU vs. FPGA for Edge AI: The Honest Answer and the Data Firehose Problem
Choosing between a GPU and an FPGA for edge AI or machine vision isn’t about which technology is more advanced. It comes down to how quickly a system needs to sense, process, and respond. Some applications can wait. For others, latency means failure.
Where GPUs Excel
Many machine vision tasks don’t need a GPU at all; a traditional camera-and-PC setup or a smart camera handles them fine. A GPU earns its place when the job demands heavier processing at the edge, such as running deep learning models or searching large, high-resolution images.
Barcode reading, though perhaps not an obvious example, can fit this mold. In a 12 MP image, the system must first locate every barcode or fiducial before it can decode or measure anything. Warehouse pallet tracking fits too. The system has seconds, not milliseconds, to identify a load on a forklift and report its location, and a 15–20 watt GPU is no burden on a forklift.
Platforms such as Allied Vision’s Alecs open smart camera, which pairs a 5 MP or 12 MP sensor with an NVIDIA Jetson and built-in lighting in a sealed enclosure, make these deployments easier. At around 40 milliseconds of latency and 25–50 frames per second, this class of hardware handles them comfortably.
Software is also faster to develop. Python or C++ code can be changed and retested in minutes, while an FPGA design must be translated into hardware logic, simulated, and rebuilt with every change. If the work can be done in software, it should be.
Where FPGAs Become Necessary
Once an application demands sub-millisecond response or hundreds to thousands of frames per second, as in closed-loop control, FPGAs need to be on your radar. The same is true when camera data rates approach 50–100 gigabits per second, where storing data to process later adds latency many applications can’t tolerate. Either way, processing must happen at the sensor, as the data arrives.
That’s what FPGAs are built for. An FPGA runs the algorithm as a dedicated hardware pipeline, processing pixels in parallel as they stream off the sensor and finishing each frame before the next arrives, typically with less power and heat than a GPU. Nearly every machine vision camera already contains one, though most are only big enough to move data to the host. Cameras with larger FPGAs, such as Concurrent EDA’s GigaSens, can run the algorithm inside the camera itself.
Laser-based applications show why this can be necessary. Melt pool monitoring in additive manufacturing adjusts laser power in real time based on the size of the pool. Laser tracking in military and scientific systems corrects a target’s position frame by frame. And in 3D laser-line metrology, each frame captures just one line of the final scan. In each case, a GPU’s latency or frame rate falls short.
The same logic applies whether the firehose is pixels, network packets, or RF samples. And no in-house FPGA expertise is required: Concurrent EDA can take a Python or MATLAB model, prototype it in software, and convert it into FPGA logic inside the camera. Bring us your data rate or latency problems your system can’t handle and we’ll help find a solution.
Watch the full Manufacturing Matters podcast episode with Ray Hoare here. Not sure which side of the line your application falls on? Contact Concurrent EDA to talk through your idea.
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