Edge Impulse

Edge Impulse is a platform operating in the field of embedded machine learning. Founded in 2019 by Zach Shelby and Jan Jongboom, its headquarters are located in Santa Clara, California, United States. As of 2025, the company has been acquired by Qualcomm and continues its operations under the Qualcomm umbrella. Edge Impulse provides services to help developers create machine learning models that run on low-power devices using sensor data.

Edge Impulse was founded in 2019 by Zach Shelby and Jan Jongboom. The company’s founding mission was to provide a platform that enables developers to rapidly create, train, and deploy machine learning models on embedded systems. The founding team had prior experience in IoT and software development, with a particular focus on expanding artificial intelligence applications on low-power devices. The company’s headquarters are located in Santa Clara, California, United States. Since its inception, Edge Impulse has collaborated with multiple industries to deliver AI-powered solutions in healthcare technology, smart industry, consumer electronics, and urban infrastructure. As of 2025, Edge Impulse was acquired by Qualcomm and began operating under its corporate structure. This acquisition has enabled the platform to offer broader hardware support and integration options on a global scale.

The Edge Impulse platform enables deployment across a wide range of hardware including microcontrollers (MCUs), graphics processing units (GPUs), central processing units (CPUs), neural processing units (NPUs), and Docker containers. The platform is designed to integrate with Python SDKs, C++ libraries, and low-code tools. Tools such as the EON Compiler and EON Tuner allow developers to optimize model size, memory usage, and processing latency.

Edge Impulse is used in industrial manufacturing for applications such as assembly line inspection, worker safety monitoring, part tracking, component fault detection, smart machinery, and predictive maintenance. These applications rely on real-time analysis of sensor and image data to ensure uninterrupted production processes and improved efficiency. In particular, processing audio, temperature, pressure, and accelerometer data enables early detection of maintenance needs.

In healthcare, Edge Impulse delivers personalized health monitoring and analysis by processing biometric sensor data. Applications such as heat stroke detection, fall detection, sleep quality measurement, offline medical device usage in rural areas, and rapid diagnosis using imaging data can be implemented via the platform. These applications typically run on continuously powered devices such as smartwatches and wearables.

The platform is used in smart building and city systems to enhance energy efficiency, ensure security, and monitor infrastructure continuity. Examples include glass break detection, room occupancy monitoring, predictive maintenance of HVAC systems, thermostats that automatically adjust based on environmental conditions, and home systems controllable via voice commands.

Edge Impulse provides high-precision functions such as water level detection, early leak detection, capacitor failure prediction, and food classification through AI models optimized for white goods, kitchen appliances, and other smart home products. Examples such as image-based coffee machine control enable end-to-end machine learning applications.

Edge Impulse offers built-in tools for collecting, visualizing, and analyzing sensor data. Issues with data quality can be identified through the platform, and data preparation processes can be automated. Advanced digital signal processing (DSP) algorithms are applied to the collected data to improve model performance. Additionally, the EON Tuner tool enables the creation of DSP and model configurations tailored to memory and latency requirements.

The platform enables the identification of signals that deviate from models trained exclusively on normal data. Visual anomalies or deviations in high-dimensional data can be detected using models optimized to run on devices. Models developed for computer vision applications such as FOMO (Faster Objects More Objects), FOMO-AD, and YOLO-Pro provide object recognition and counting capabilities that operate efficiently on microcontrollers.

Edge Impulse offers various plans tailored for both individual developers and enterprise teams. The platform provides advantages in privacy and speed by processing data directly on the device without requiring cloud connectivity. It operates with SOC2-compliant security protocols. Configurable dashboards and continuous improvement support facilitate project tracking.

Edge Impulse collaborates with semiconductor, hardware, and software providers such as NVIDIA, STMicroelectronics, Arduino, Nordic Semiconductor, AWS, and Advantech. These partnerships enable the development of end-to-end solutions and seamless integration of the Edge Impulse platform with diverse hardware. As of 2025, Edge Impulse was acquired by Qualcomm. This merger has expanded the platform’s support for hardware such as the Qualcomm Dragonwing RB3 Gen 2 and created a more integrated development environment with Qualcomm AI Hub. Additionally, integrations with NVIDIA Jetson and Omniverse Replicator have been incorporated into the platform to enable synthetic data generation and virtual testing environments.

Edge Impulse aims to make embedded AI solutions more widespread, accessible, and production-oriented. The company’s future strategy is based on deeper collaboration with developer communities and industrial stakeholders to automate and simplify the processes of data collection, model training, optimization, and deployment. Following its merger with Qualcomm Technologies, the platform’s future direction focuses on expanding support for running advanced computer vision, speech recognition, and natural language processing models on high-performance hardware such as Qualcomm’s Dragonwing series processors. Additionally, Edge Impulse plans to expand synthetic data generation, virtual testing environments, and end-to-end device integration. In the long term, the platform seeks to contribute to the adoption of edge AI technologies across all sectors by making product development faster and more accessible across a broad spectrum—from low-power microcontrollers to complex industrial systems. Educational tools and low-code development environments are targeted to onboard new user groups. Edge Impulse continues to support this vision by focusing on automation, data privacy, and on-device performance optimization to accelerate the commercialization of edge AI solutions.

Edge Impulse is a versatile and adaptable platform that facilitates the development of embedded AI applications and is used across a wide range of domains—from industry and healthcare to consumer electronics and smart city infrastructure. The platform provides tools, algorithms, and integration solutions to enable rapid and reliable development of machine learning models for devices operating at the edge.