Atomo Innovations · Edge AI Runtime

Inference, compiled
down to the metal.

ASNN: Atomic Software Neutral Network

A runtime for running trained models on embedded edge hardware. Convert once, deploy across ARM, x86, NPU and GPU targets, and get real-time inference without a round trip to the cloud.

<10ms
Median latency
4
Target architectures
Py
SDK language
View workflow → View spec sheet

Why ASNN

Built for hardware, not for demos

Most inference stacks are tuned for a workstation GPU. ASNN is tuned for the device it actually ships on: a camera, a controller, a sensor node with a fixed power and memory budget.

Throughput

High-performance inference

Execute neural network models with optimized speed for demanding real-time workloads, without offloading to the cloud.

Acceleration

Hardware-accelerated processing

Leverage on-device neural processing units for maximum throughput per watt.

Latency

Real-time analytics

Process video streams and sensor data with minimal latency for live decision-making at the point of capture.

Footprint

Edge-optimized runtime

Reduce cloud dependency and bandwidth cost by running inference where the data is generated.

Tooling

Python-based development

Build and deploy AI applications using a simple, flexible Python API, no custom firmware required.

Efficiency

Constrained-resource execution

Achieve high throughput while keeping CPU and memory usage inside embedded-device budgets.

Spec sheet

What ships in the SDK

The full toolchain, from model conversion to a production inference pipeline.

Model conversion & optimization
Real-time inference engine
Hardware-accelerated AI processing
Python SDK and APIs
Multi-threaded processing
Object detection
Image classification
Semantic segmentation
Pose estimation
Video analytics processing
Memory optimization
Embedded Linux support
Custom model deployment
Low-latency inference pipeline
High-throughput processing

Application areas

Deploy where the decision needs to happen

From surveillance to robotics, ASNN runs the same model API across very different device classes.

Smart surveillance

Intelligent video analytics for security and monitoring applications.

  • Person detection
  • Intrusion detection
  • Object tracking
  • Perimeter monitoring
  • Crowd analytics
  • Occupancy monitoring

Intelligent transportation

Traffic and mobility analytics computed directly at the roadside.

  • Vehicle detection
  • Vehicle classification
  • Traffic monitoring
  • Lane occupancy analysis
  • Flow analytics
  • Incident detection

Manufacturing & automation

Vision systems integrated directly into automated production lines.

  • Quality inspection
  • Defect detection
  • Assembly verification
  • Process monitoring
  • Equipment monitoring
  • Predictive maintenance

Safety monitoring

Real-time monitoring systems for operational and worker safety.

  • PPE detection
  • Worker monitoring
  • Restricted-area detection
  • Fall detection
  • Hazard identification
  • Occupancy analysis

Smart infrastructure

Monitoring and management of facilities and critical assets.

  • Facility monitoring
  • Asset tracking
  • Environmental monitoring
  • Infrastructure inspection
  • Resource optimization
  • Equipment analytics

Robotics & autonomous systems

Real-time perception for AI-powered robotics and autonomous devices.

  • Visual navigation
  • Object recognition
  • Human detection
  • Machine vision
  • Autonomous monitoring
  • Intelligent automation

Development workflow

Six stages, training to production

Order matters here: each stage produces the input the next one needs.

Train

Train models in TensorFlow, PyTorch, or ONNX.

Convert

Convert and optimize using ASNN's model tools.

Deploy

Push optimized models to edge devices.

Develop

Build applications with the Python API.

Execute

Run real-time, hardware-accelerated inference.

Tune

Monitor and optimize resource utilization.

SDK components

Every layer of the stack

From model conversion to the utilities that keep a fleet of devices running.

Model conversion tools

Convert and optimize models for embedded edge hardware.

Runtime engine

Execute inference with low latency directly on the device.

Python SDK

Build AI applications with a simple, flexible API.

Performance analysis tools

Inspect inference speed, latency, and hardware utilization.

Sample applications

Reference implementations to start development from.

Deployment utilities

Manage application rollout across a device fleet.

Developer-friendly

Build faster with Python

ASNN gives you a Python development environment for building, testing, and deploying AI applications quickly.

Integration with OpenCV and common AI frameworks keeps the developer workflow simple without giving up inference performance.

PythonOpenCVTensorFlowPyTorchONNXEmbedded Linux
infer.pyASNN 3.2
# quick inference example
import asnn
import cv2

# load an optimized model
model = asnn.load("detector.asnn")

# run inference on a frame
frame = cv2.imread("input.jpg")
results = model.infer(frame)

for det in results:
    print(det.label, det.confidence)