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Edge Impulse

Edge Impulse provides a platform for developing embedded machine learning models that run on various edge devices, including microcontrollers and gateways. This technology enables manufacturers to optimize sensor data processing, reduce bill of materials costs, and accelerate time to market for their products.

Traction 10/10
San Jose, United States · HQ
Founded 201910830K+ followers
  • Artificial Intelligence
  • Developer Tools
  • Enterprise Software
  • Internet of Things
  • Software Only
Updated 3 days ago

Funding

Raised to date

$54.4MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

Funding rounds are not available yet.

Founders

1 founder

Spencer Huang

Chief Revenue Officer

Product

Problem

Developing and deploying machine learning models on edge devices is challenging due to resource constraints, complex toolchains, and the need for specialized expertise. Optimizing sensor data processing, reducing bill of materials costs, and accelerating time to market for edge AI products requires a streamlined development platform.

Solution

Edge Impulse provides a comprehensive platform for developing and deploying embedded machine learning models on a variety of edge devices, from microcontrollers to gateways. The platform simplifies the process of building, deploying, and scaling embedded ML applications by offering tools for data ingestion, labeling, feature extraction, model training, and deployment. It enables developers to optimize sensor data processing, reduce BOM costs, and accelerate time to market for next-generation edge AI products and solutions.

Target Audience

The primary users are product leaders, AI practitioners, and embedded engineers looking to bring intelligence to edge devices across various industries, including manufacturing, operations, product development, and transportation.

Features

  • End-to-end platform for data collection, labeling, feature extraction, model training, and deployment
  • Support for a wide range of edge devices, including MCUs, NPUs, CPUs, GPUs, gateways, sensors, and cameras
  • Tools for optimizing AI models for tiny devices, including the EON Tuner and EON Compiler
  • Algorithms such as FOMO (Faster Objects, More Objects) and GMM Anomaly Detection
  • Data Explorer for visualizing and analyzing sensor datasets
  • Capability to generate synthetic datasets using GenAI
  • Customizable performance settings to meet device constraints
  • API access for integration with existing systems
This profile is AI-generated and may contain inaccuracies.