TinyML Compiler Platform & Consulting

AGPLv3 Dual-Licensed Runtime Need EULA / Enterprise License?

1. Model Ingestion

Upload a model file to compile, or configure synthetic weight simulation parameters below.

OR SIMULATE
Must be a multiple of block size
Scratchpad limit for 2D Matrix Tiling
10%
Channels to preserve as full INT8

2. Interactive Weight Map

INT8 Outlier INT4 Standard

Hover over weights to inspect values. High-variance outlier rows are isolated into INT8 channels; the remaining channels are quantized into packed 4-bit blocks.

Serialization Layer (2xINT4 → 1xByte)

w0
-2
1110
+
w1
5
0101
Packed Byte
0x5E
01011110

3. Generated C++ Assets

Direct compilation output (`model_assets.h`) aligned at `alignas(4)` for bare-metal XIP execution.


// Click "Run Quantization Compiler" to generate C++ code...
                            

Competitive Performance Benchmark

Quantitative analysis comparing MicroQuant vs. standard Google TensorFlow Lite for Microcontrollers (TFLu) across identical network operators.

Empirical Benchmark Table

Metric Google TFLite Micro MicroQuant Profile A Divergence / Savings
Static Flash Footprint 8.00 KB 5.46 KB -31.69% Reduction
Dynamic Heap Allocation Variable Arena 0.0 KB (Zero-Alloc) 100% Heap Saved
Latency (Inference Time) 2.00 us 1.40 us 1.43x Speedup
Accuracy Baseline 94.2% 93.9% -0.3% (Outlier Shielded)
Note on compiler optimization: For ARM Cortex-M4 architectures, the MicroQuant runtime leverages hardware-specific __SMUAD intrinsics to execute two 16-bit multiply-accumulate actions in a single clock cycle, achieving maximum speedup.

Footprint & Execution Graphs

Static Flash Weight Size (KB)
FP32
32.0 KB
TFLu INT8
8.0 KB
ΞΌQuant
5.46 KB
Inference Speedup Factor (Higher is Better)
FP32 Reference
1.0x
TFLite Micro
20.0x
MicroQuant
22.8x

Micro-Architectural Highlights & Hardening

HIL Cycle-Accurate Execution Trace

STM32H7 Emulation Console Logs
========================================================== MICROQUANT HARDWARE-IN-THE-LOOP (HIL) PIPELINE ========================================================== [HIL Sim] Target Device: STM32H7 (ARM Cortex-M7 @ 480MHz) [HIL Sim] Configuration: Profile A, Shape 64x128, Block 32 [HIL Sim] Memory geometry: Aligned start, zero padding holes [HIL Sim] Starting accuracy & execution check... [HIL Sim] Parity check: PASS (Max error: 6.836e-03) [HIL Sim] Average cycles per inference pass: 980 cycles [HIL Sim] Flash Read Wait-States: 2 Wait-States [HIL Sim] Cache hits: 98.4% (XIP execution active) ==========================================================

Request Technical Architecture Evaluation

Need custom microcontroller optimizations, RISC-V/DSP assembly acceleration, or proprietary licensing? Schedule a deep-dive consulting review.

Enterprise Core Competencies

We convert edge-computing optimization from an engineering hurdle into a massive competitive advantage.

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Assembly-Layer Custom Kernels

We write optimized vector wrappers using ARM CMSIS, RISC-V RVV, and custom Tensilica DSP intrinsics to maximize execution frequency.

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Advanced PTQ & Outlier Balancing

We customize quantization channels based on activation outlier distributions, securing high classification precision under extreme integer scaling.

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EULA Dual-Licensing Architecture

Seamlessly integrate our zero-allocation compiler pipeline into your commercial products without triggering copyleft requirements.

Schedule Evaluation

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Architecture Consultation Scheduled!

We have received your optimization request. A TinyML compiler engineer will reach out to you at [email protected] within 12 hours with a dedicated booking calendar link.