ESP32-P4 Deep Learning Pipeline update1: Optimizing AI Models for Embedded Devices

Alright, so I’ve been brewing this project in my head for a while now, and I’m finally diving into it! The goal? To create an entire end-to-end pipeline to prune, quantize, optimize, and deploy deep learning models on embedded devices, specifically targeting the ESP32-P4. Sounds crazy? Maybe. Exciting? Absolutely.

This project isn’t your usual "just quantize the model and hope it works" thing. Nope. I want something modular, efficient, and portable that actually uses the full power of the hardware. And, of course, it needs to be fun to build, because why else am I doing this? Let me walk you through what I have in mind.

Step 1: Building the Pruning and Quantization Tool

First up is creating a tool in PyTorch that doesn’t just prune and quantize it shreds the model down while keeping its brain intact. I’m aiming for:

• Pruning 80% of the weights. Yes, 80%! Sounds aggressive, I know, but with the right structured pruning techniques and careful retraining, it’s totally possible.
• Quantization to INT8. Not just weights, but operations and activations too. The entire thing in INT8. And here’s the catch: it’s all quantization-aware training, so the model learns to behave well even with reduced precision.

By the end of this step, I’ll have a graph-based model representation that’s lean, mean, and ready to be deployed. The graph format will make life easy for the next steps.

Step 2: Layer Implementation & Code Generation

Here’s where the real fun starts: writing modular, hardware-aware code for each layer of the model. Since my target is the ESP32-P4, I’m going to leverage its SIMD instruction set for max performance.

“Writing ASM is a bit of a pain (okay, a lot of pain), but it’s worth it to squeeze every last drop of performance from the hardware.”

Here’s the plan:

• For every critical part of a layer (like matrix multiplication, convolutions, etc.), I’ll write an optimized ASM implementation.
• I’ll also keep a C implementation of each layer for debugging, because let’s be real, debugging ASM is a nightmare.

Once the layers are ready, I’ll build a code generator. This thing will take the graph from Step 1 and spit out C++ code that links everything together: layers, operations, weights you name it. By the end of this step, the model will be running on the ESP32-P4.

Step 3: Taking It to the Next Level with MLIR

Now comes the cool, nerdy part using MLIR to optimize the model graph even further. MLIR is like this magic toolkit for building your own compiler pipeline, and I’m going to use it to squeeze out every bit of performance possible.

Here’s what I’m planning:

1. Convert the graph and layers into MLIR: Using Polygeist, I’ll convert my modular layers into high-level MLIR representations. This bridges my C++ code with the MLIR world.
2. Apply optimization passes: Custom passes will handle tasks like:
   • Moving conditions out of loops.
   • Exploiting sparsity in pruned weights (why compute zeros, right?).
   • Fusing layers into single kernels for efficiency.
   • Pushing intermediate data to faster memory (TCM, if available).
   • Smart unrolling, loop optimization, and more.
3. Lower to LLVM IR: Once optimized, the MLIR code will be converted to LLVM IR, which generates hardware-specific ASM for the ESP32-P4.

Why I’m So Hyped About This

This isn’t just about squeezing AI models onto tiny devices. It’s about building something powerful and reusable. By making the whole pipeline modular—layer definitions, ASM optimizations, MLIR passes I’m setting up a system that can adapt to any hardware target. Today it’s the ESP32-P4; tomorrow, it could be RISC-V, ARM, or something else entirely.

Challenges? Oh, There Will Be Plenty

Let’s not sugarcoat it: this project is going to be a beast.

• Writing ASM for SIMD operations will take time and debugging it will probably give me a few headaches.
• MLIR passes can get complicated fast. Designing them to work together without breaking something else will be tricky.
• Quantizing and pruning without killing accuracy is always a fine line. But hey, that’s the fun part, right?

Final Thoughts

Yeah, it’s ambitious. Yeah, it’s going to be tough. But I’m all in. This is the kind of project that pushes you to learn, to experiment, and to build something you can be proud of. If you’ve read this far, thanks for coming along for the ride! I’ll be sharing updates as I go, so stay tuned.