Real-time segmentation on edge - faster, smaller, more precise.

The Challenge

Segmentation is easy in theory. Hard on edge devices.

Real-time segmentation models face three core problems:

• Loss of detail (e.g. fingers, small objects)

• Motion blur instability

• Incorrect classification of closed areas

At the same time:

• hardware is constrained (latency, memory, power)

• reducing model size often kills accuracy

You usually have to choose between speed and quality.

The Approach

We didn’t shrink the model. We redesigned it.

Instead of naive optimization, NeuralSpike:


1. Built a custom architecture

   • Based on MobileNetV3

   • Designed for edge constraints from the start

2. Used automated architecture search

   • Generated multiple candidate models

   • Benchmarked directly on target hardware

   • Selected only architectures that met performance thresholds

3. Optimized structure - not just size

   • Smart branching (bifurcation) and merging

   • Preserved fine details without sacrificing speed

Result: A model tailored to the device, not just the dataset.


The Solution

A segmentation model that actually runs on edge devices.

• 172K parameters (vs 206K baseline)

• Optimized for real-time inference

• Designed for embedded deployment

Runs on:

• MediaTek Genio (edge)

• CPU (browser / x64)

Performance Results

Benchmark-driven. Not guesswork.

What this means:

• Faster inference - real-time decisions

• Smaller model - easier deployment

• Better accuracy - fewer errors in production

Visual Quality (What actually improved)

Speed is easy. Precision is harder. We improved both.

NeuralSpike model:

• Better segmentation of small details (e.g. fingers)

• More stable results under motion blur

• More accurate global structure understanding

Why it worked

Edge performance is an architecture problem - not just optimization.

Key insight:

• Reducing parameters blindly - kills accuracy

• Smart architecture design - improves both speed and precision

NeuralSpike approach:

• architecture search & benchmarking loop

• training only promising candidates

• optimizing for target hardware from day one

Built for real edge hardware

MediaTek Genio platform:

• energy-efficient edge AI processing

• integrated NPU for real-time inference

• designed for embedded deployments

Why it matters:


This is not a lab demo - this is production-ready hardware.

What this means for you


This is how we deliver edge AI that actually works.


With NeuralSpike you get:

• Models designed for your hardware

• Measured performance (FPS, latency, memory)

• Real-time operation on-device

• Faster path to deployment