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Edge Device

MCUs, NPUs, CPUs, GPUs
Gateways
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Build datasets, train models, and optimize libraries to run directly on device; from the smallest microcontrollers to gateways with the latest neural accelerators (and anything in between).

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A circular Edge Impulse workflow showing the steps to create machine learning for edge devices: Build datasets, Train models, Optimize libraries, and Deploy to hardware. The steps are connected in a continuous loop around the Edge Impulse logo.
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Edge Impulse client Chamberlain logoEdge Impulse client Halma logoEdge Impulse client Polar logoEdge Impulse client Ultrahuman logo
Edge Impulse client Chamberlain logoEdge Impulse client Halma logoEdge Impulse client Polar logoEdge Impulse client Ultrahuman logo
Edge Impulse client Chamberlain logoEdge Impulse client Halma logoEdge Impulse client Polar logoEdge Impulse client Ultrahuman logo

Bring Intelligence to Any Edge Device

Edge Impulse, a leading edge AI development platform, brings intelligence to edge devices. Our platform and in-house experts will accelerate your delivery of next generation products and solutions.

Unlock sensor data

Faster time to market

Remove hidden complexities and tedious repetitive steps

Quickly progress high-value tasks that lead to commercialization

De-risk model development with agnostic and scalable edge AI tools

Screenshot of Edge Impulse’s data acquisition interface. It shows a dashboard for collecting and labeling sensor data, with a list of recorded samples including names, labels, timestamps, and durations. On the right, there are controls to record new data from a device and sensor, and a chart displaying raw accelerometer data across three axes (X, Y, and Z).

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Edge AI for

Manufacturing and Operations Environments

Edge AI offers immense potential to mitigate significant financial losses by addressing issues such as machine downtime, inefficiency, and quality control problems through early anomaly detection. Deploying Edge AI at the data source enhances security, reduces latency, and accelerates decision-making for service operators, quality control teams, and other stakeholders, ultimately improving quality and efficiency.

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Graphic showing Edge Impulse resources for industrial and manufacturing use cases. The main card is titled “Edge Machine Learning for Industrial and Manufacturing Environments” and includes a photo of robotic arms working on a car assembly line with sparks flying. Behind it is another card titled “When Is Edge AI Necessary?”
Edge AI for

Product
Development

Fast-track development. and dramatically increase the chance of the successful edge AI deployment. Quickly iterate to validate ideas on a platform that promotes cross-team collaboration.

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Image showing wearable and mobile health monitoring interfaces powered by Edge AI. A smartwatch and smartphone screens display health data such as cough frequency, heart rate, stress levels, and activity summaries. Bar charts show cough counts over time, while circular gauges track metrics like heart rate and body battery
Edge AI for

Transportation

Edge AI is needed in the vast world of transportation from smart city intersections to inspecting bridge and road conditions to vehicle performance and safety to railway safety and security. These mission critical environments are not tolerant of latency and require the highest level of security in a small form factor, all key features in edge AI solutions.

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Image illustrating Edge AI for transportation. The top section shows a close‑up of a car wheel with AI object detection labels identifying the tire and multiple lug nuts, each with confidence scores. The bottom section shows an Edge Impulse dashboard displaying model testing data, classification results, and a 100% accuracy score.
Edge AI for

Industrial

With Edge Impulse, industrial enterprises and equipment manufacturers are adding new insights to their sensor networks through embedded machine learning.

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Image illustrating Edge AI for industrial environments. The top shows a factory worker using a handheld power tool on an assembly line. The bottom shows Edge Impulse’s Data Explorer interface with a scatter plot of sensor data points, labeled as anomalies and non‑anomalies, used to train and validate embedded machine learning models.

Any

Data.

Any

Model.

Any

Edge Device.

Accelerate Innovation with Seamless Collaboration for Production-Ready Models

Workflow diagram showing Edge Impulse’s end‑to‑end process for deploying AI to edge devices. The top row shows Data Collection, Edge Devices, and Monitor connected in a loop. The bottom row shows Build, Train, and Optimize steps: build high‑quality datasets, train DSP and AI models within constraints, and optimize models for any edge device. Arrows connect all steps to illustrate a continuous development cycle

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