FPGA vs GPU vs Edge TPU: Choosing the Right ML Hardware

At Myelin, we've been exploring different hardware targets for edge inference. We started with Raspberry Pi (ARM CPU), moved to Jetson Nano (GPU), tried Coral Edge TPU, and then someone said "hey, what about FPGAs?" That someone was my manager. I wish he hadn't.

Look, I'm going to give you the honest version. Not the vendor marketing version.

Jetson Nano (GPU)

The Jetson Nano was the easiest win. It's basically a baby GPU with CUDA support, 128 Maxwell cores, and 4GB of shared RAM. If you already know PyTorch or TensorFlow, deployment is straightforward. TensorRT optimizes your model, and you get solid performance.

Real numbers from our super-resolution model:

• Inference time: 18ms (256x256 input)
• Power consumption: 5-10W
• Cost: ~$99 USD
• Setup time: One afternoon

The downside? 10 watts is a lot for battery-powered applications. And the Nano is discontinued now, so the Jetson Orin Nano is the way to go. But in early 2021, the Nano was our go-to.

Coral Edge TPU

Google's Edge TPU is a tiny chip designed specifically for INT8 inference. You can get it as a USB accelerator or on the Coral Dev Board. It's purpose-built for TFLite models and it shows.

Same super-resolution model (INT8 quantized):

• Inference time: 8ms
• Power consumption: 2W
• Cost: ~$60 (USB accelerator)
• Setup time: A couple hours

The catch is you're locked into TFLite and INT8 (I wrote a detailed comparison of TFLite vs ONNX Runtime if you're evaluating runtimes). Every operation in your model needs to be supported by the Edge TPU compiler. If even one op isn't supported, that layer runs on the CPU instead, and your latency spikes. I spent a full day figuring out why our model was slow, only to discover that a single tf.image.resize with bilinear interpolation was falling back to CPU.

Pro tip: always run edgetpu_compiler and check the log for unsupported ops before assuming you'll get full TPU acceleration.

FPGA (Xilinx)

Okay. This is where things got painful.

FPGAs are theoretically amazing for ML inference. Custom dataflow architectures, configurable precision, incredible power efficiency. The keyword there is "theoretically." In practice, the toolchain is a nightmare.

We tried deploying to a Xilinx ZCU104 using Vitis AI. Here's what that journey looked like:

1. Install Vitis AI (took a full day because of dependency issues)
2. Quantize the model using Vitis AI quantizer (different from TFLite quantization)
3. Compile to a DPU (Deep Processing Unit) overlay
4. Debug cryptic errors for three days
5. Get it running, realize the supported op list is even more restrictive than Edge TPU
6. Cry a little

Same model on FPGA:

• Inference time: 6ms (when it worked)
• Power consumption: 3-5W
• Cost: $800+ for the dev board
• Setup time: Two weeks (and I'm being generous)

The latency and power numbers are great. But the development velocity is terrible. Every model change requires re-synthesis, which can take hours. Compare that to TFLite where you swap a model file and restart.

The Honest Comparison

	Jetson Nano	Coral Edge TPU	Xilinx FPGA
Latency	Good	Great	Best
Power	High	Low	Medium
Cost	$99	$60	$800+
Dev Experience	Excellent	Good	Painful
Flexibility	High	Medium	Low (initially)
Ecosystem	Mature	Growing	Niche

My Recommendation

For 90% of edge ML projects, just use a Jetson or Coral device. Seriously. The development speed difference is massive.

Use an FPGA if you have a very specific, high-volume production deployment where the power efficiency and custom architecture matter at scale. Think automotive, defense, or telecom. For prototyping and small-batch industrial IoT? Not worth the pain.

I told my manager this after two weeks of FPGA wrestling. He agreed. We went back to Jetson for the production deployment and everyone was happier. Sometimes the boring choice is the right choice. For our anomaly detection system on Raspberry Pi, we didn't even need a dedicated accelerator -- a well-quantized model on the ARM CPU was plenty fast. I think the FPGA exploration was still valuable because it taught me how inference hardware actually works at a low level. But would I do it again voluntarily? Probably not until the toolchains improve significantly.