Inspiration
Every day, billions of low-cost microcontrollers power industrial monitors, wearables, agricultural sensors, and connected devices. Many operate with only a few hundred kilobytes of SRAM and limited flash, making them fundamentally different from the hardware used to run modern machine-learning models.
The usual cloud-first approach creates its own constraints. Streaming raw sensor data adds network latency, consumes energy and bandwidth, and can expose sensitive data outside the device. Running inference locally is a better architectural choice, but deploying neural networks onto bare-metal microcontrollers remains unnecessarily manual.
Embedded developers must deal with model conversion, quantization, tensor lifetimes, memory sizing, hardware constraints, and firmware integration separately. General-purpose ML runtimes can also introduce runtime overhead that is difficult to justify on extremely constrained devices.
We built Shannon to move this work from manual firmware engineering into a deterministic compilation pipeline.
Instead of carrying a general-purpose ML runtime onto the microcontroller, Shannon analyzes the model at compile time, plans its memory usage, checks whether it fits the selected hardware, and emits static inference code.
Our goal is simple:
Compile intelligence down to the silicon.
What It Does
Shannon is a TinyML static compiler for resource-constrained microcontrollers.
Developers can provide a supported ONNX model or Shannon IR JSON graph, or select a built-in reference model. Shannon parses the model into an intermediate representation containing layers, tensors, shapes, parameters, and memory information.
The model is then converted using symmetric INT8 post-training quantization. FP32 weights are represented as 8-bit integers, reducing weight storage by approximately 75% compared with their original 32-bit representation.
Shannon's key feature is its static SRAM arena planner.
Instead of allocating activation tensors dynamically during inference, Shannon analyzes tensor lifetimes and assigns reusable memory offsets to tensors whose lifetimes do not overlap. This produces a single statically planned activation arena and verifies that active tensors do not collide in memory.
The compiled model is then evaluated against supported MCU hardware profiles, including ESP32-S3, STM32H7, RP2040, nRF52840, and Teensy 4.1. Shannon reports SRAM usage, flash usage, and estimated execution latency.
Finally, Shannon generates C inference code containing quantized parameters, a static tensor arena, integer kernels, and an inference entry point. The generated output is checked for dynamic memory allocation.
The complete workflow is:
MODEL ↓ PARSE ↓ SHANNON IR ↓ INT8 QUANTIZATION ↓ SRAM ARENA PLANNING ↓ HARDWARE FIT ↓ VALIDATION ↓ GENERATED C ↓ FIRMWARE INTEGRATION
How We Built It
Shannon is built as a decoupled compiler workstation with a Python compilation backend and an interactive browser interface.
The core compiler is implemented in Python using FastAPI. It represents models through a custom intermediate representation containing tensors, layers, shapes, datatypes, weights, memory offsets, MAC counts, and execution-cost information.
The compilation pipeline consists of four major stages.
- Model Parsing
Shannon accepts structured model definitions and supported ONNX protobuf models. The ONNX parser extracts graph connectivity, tensor metadata, and initializers while explicitly rejecting unsupported operators.
- Quantization
A symmetric INT8 quantization stage converts model parameters into compact integer representations and computes the required quantization parameters.
- Static Memory Planning
The memory planner tracks tensor lifetimes and reuses physical SRAM regions whenever tensor lifetimes do not overlap. A verification stage checks the resulting layout for memory collisions.
- Code Generation
Shannon emits a C header containing quantized weights, a statically allocated tensor arena, integer inference kernels, and the inference entry point. The generated inference path avoids runtime heap allocation.
The frontend is built with React, TypeScript, and Vite. It exposes the compilation process as an engineering workstation with an interactive model graph, quantization analysis, SRAM arena visualization, hardware-fit matrix, validation results, generated C, and firmware integration.
We also implemented a Silicon Copilot that can interpret compiler telemetry and provide contextual optimization guidance based on the compiled model and selected hardware target.
Challenges We Ran Into
Our biggest technical challenge was compile-time memory planning.
Neural networks can contain intermediate tensors with overlapping lifetimes, branches, and skip connections. Allocating every tensor independently wastes scarce SRAM, while reusing memory too aggressively can overwrite data that is still required.
We therefore implemented tensor lifetime analysis followed by interval-based memory allocation. Shannon then performs a separate verification pass over the resulting layout to detect collisions.
Another challenge was maintaining deterministic compiler behavior.
For embedded systems, reproducibility matters. The same model should produce predictable memory requirements and generated artifacts. We added regression tests that repeatedly compile models and verify consistent flash usage, SRAM allocation, and generated C output.
Supporting real model input introduced another challenge.
A compiler cannot safely assume that every uploaded model is valid or supported. Shannon's ONNX parser extracts actual model information and explicitly rejects unsupported operators rather than silently replacing them with another implementation.
We also wanted the generated output to have predictable memory behavior. The generated C is checked for dynamic allocation calls such as malloc, calloc, realloc, and free, while verifying that a static tensor arena is emitted.
Accomplishments That We Are Proud Of
We built a working TinyML compilation pipeline that combines model parsing, INT8 quantization, static SRAM planning, hardware-fit analysis, validation, and C code generation into one workflow.
Our strongest accomplishments are:
• Genuine ONNX model parsing with explicit unsupported-operator rejection.
• Symmetric INT8 quantization with approximately 75% reduction in FP32 weight storage.
• Lifetime-based SRAM arena planning with tensor buffer reuse.
• Static inference memory with zero dynamic heap allocation in generated inference code.
• Memory-collision verification for the generated arena.
• Deterministic compilation and regression testing.
• Hardware-fit analysis across multiple constrained MCU profiles.
• Generated C artifacts that can be inspected, edited, copied, and exported.
• Automated regression tests covering parsing, memory planning, determinism, allocation safety, and code generation.
Most importantly, Shannon turns several disconnected embedded-engineering tasks into one visible workflow:
Model → Quantize → Memory → Hardware → Validate → C
What We Learned
Building Shannon changed how we think about TinyML deployment.
Our biggest lesson was that memory is not merely a resource to measure after compilation. It can be treated as a compile-time scheduling problem.
Once tensor lifetimes are known, SRAM can be planned before inference begins. This makes memory usage deterministic and creates opportunities for aggressive buffer reuse without introducing runtime allocation.
We also learned that compiler transparency matters as much as optimization.
Instead of simply returning "the model fits," Shannon exposes the graph, tensor information, memory layout, hardware constraints, validation results, and generated C so developers can understand why a model fits.
Finally, we learned that embedded ML requires a different definition of optimization. A smaller model is useful, but predictable memory, deterministic execution, and inspectable generated code can be just as important as model accuracy.
What's Next For Shannon
Our next step is to expand Shannon from a focused TinyML compiler into a broader embedded ML compilation platform.
We plan to:
• Expand ONNX operator coverage.
• Add stronger target-specific kernel generation.
• Improve mixed-precision quantization with true packed low-bit representations.
• Support additional MCU architectures, including RISC-V.
• Strengthen numerical validation against reference runtimes.
• Add hardware-in-the-loop testing for measured latency and memory behavior.
• Integrate Shannon into CI/CD workflows so models can be automatically compiled and hardware-checked whenever model weights change.
Long term, we want Shannon to become the compiler layer between modern machine-learning development and constrained embedded silicon.
A developer should be able to take a trained model, understand its hardware requirements, compile it into deterministic embedded code, and know whether it fits before it ever reaches the firmware team.
Shannon's vision is simple:
Compile intelligence down to the silicon.
Log in or sign up for Devpost to join the conversation.