NPU IP family for TinyML applications in AIoT devices
Ceva-NeuPro-Nano is a highly efficient, self-sufficient Edge NPU designed for TinyML applications. It delivers the optimal balance of...
Harnessing neural networks and signal processing to accelerate Edge AI
Humans excel at cognitive processing, for example, recognizing faces, vehicle lane tracking, or separating human speech from background noise. This happens because the brain’s neural networks learn how to analyze and interpret important visual and audio cues.
Creating artificially intelligent machines with the same abilities is challenging but important in applications such as automotive safety, surveillance, voice assistants, and ambient sensing. Power efficient Edge AI deployment in neural network-based sensing designs is critical to addressing this challenge.
Ceva offers a comprehensive suite of scalable processor solutions tailored for the future of Edge AI and sensing applications, ready for SoC integration. From NPUs (Neural Processing Units) delivering 10’s of GOPS up to 1000’s of TOPS, to DSPs (Digital Signal Processors), our range of offerings cater to the demands of TinyML, Edge AI, Imaging, Vision, Radar, Audio, and Voice use cases.
These processor solutions are combined with a full AI SDK, supporting leading industry standard AI frameworks, including a Model Zoo of pretrained and optimized TinyML models, and various application software packages.
Target Applications
Automotive
Deep neural networks provide the sophisticated image processing that advanced driver assistance systems (ADAS) need to recognize signs, pedestrians, and vehicles.
Security and Surveillance
Embedded systems that offer face recognition based on neural networks are increasingly employed in camera-based surveillance. Coupled with audio sensors, neural networks can identify sounds, such as breaking glass or dogs barking, and trigger a planned response.
Augmented Reality
Real-time augmented reality applications on battery-powered mobile devices rely on deep learning and energy-efficient operation.
Smart Home
Sophisticated interpretation and response to voice commands and audio inputs by smart appliances and personal assistants depend on deep learning.
Retail Automation
Facial age and gender profiling enabling retail kiosks to match offers to customers, and natural language processing allowing them to interact, both require deep learning processing.
Healthcare
Deep learning supports audio keyword detection and natural language processing in patient diagnostic systems.
Wearables
Smart watches, smart bands, human/pet/asset trackers and earbuds are just some of the applications that requires deep learning processing.
Industrial IoT
From smart meters to smart cities, from factory floor to long-range asset tracking, all rely on deep learning and energy-efficient operation.
