Edge AI Sensing | CEVA DSP

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) to DSPs (Digital Signal Processors), our range of offerings cater to the demands of Edge AI, Imaging, Vision, Radar, Audio, and Voice use cases.

These processor solutions are combined with a full AI stack, including deep neural network (DNN) AI compiler, leading industry standard AI frameworks support, and various application software packages.

target markets

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.