We design noise-robust, ultra-compact AI architectures that deploy on $5 microcontrollers. From voice interfaces to missile defense.
Each technology addresses a distinct sensing modality with a unified design principle: structural robustness without runtime overhead.
Noise-Conditioned State Space Model for always-on keyword spotting. Five noise-conditioning mechanisms achieve structural robustness without separate denoising — works on $5 MCUs with 7ms latency.
NC-Conv extends noise-conditioning to vision. A learned quality gate blends dynamic and static convolution paths — enabling robust lane detection through fog, rain, tunnels, and low-light on an $8 MCU.
Constrained Optimization Pseudo-spectrum resolves up to 2ρ(M−1) sources from M sensors via 4th-order cumulants, coupled with Random Finite Set multi-target tracking. PHD filter with velocity-gated merging achieves 86% fewer identity switches in multi-target tracking scenarios.
5KB to 20KB INT8 models. Fits in on-chip SRAM — no external memory needed.
Deploys on $5 ARM Cortex-M MCUs. No cloud, no latency, no recurring cost.
Structural robustness baked into the architecture. No denoising module required.
Patent portfolio filed (KR + US). PCT international filing in progress.
Structurally noise-robust SSM via Spectral-Aware SSM (SA-SSM) with Δ-modulation and B-gating
Noise-conditioned SSM architecture with formal robustness analysis
2ρ(M−1) source resolution coupled with PHD multi-target tracking
Quality-gated dynamic/static path blending for adverse-condition vision
5 independent claims + 20 dependent claims covering audio, vision, and sensor modalities
Covering NC-SSM, DualPCEN, selectivity modulation, and hardware accelerator specs
NanoAgentic AI is an IP-driven research company focused on building the smallest, most robust AI models for edge deployment. Our work spans audio intelligence, computer vision, signal processing, and defense applications.
Every architecture we design follows a single principle: structural robustness without runtime overhead. Rather than bolting on denoising modules or augmentation hacks, we engineer noise immunity directly into the model's computation graph.
Our technologies are protected by a growing patent portfolio and validated through peer-reviewed publications at top-tier venues including IEEE, Interspeech, and ACCV.
We license our technologies for commercial deployment. Let's talk.
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