CEVA, a licensor of signal processing IP for smarter, connected devices, today announced that Novatek Microelectronics, a fabless IC design house in Taiwan, has licensed and deployed the CEVA-XM4 intelligent vision DSP for its next-generation vision-enabled System-on-Chips (SoCs) targeting a range of end markets requiring advanced visual intelligence capabilities.
Novatek's current camera SoC lineup for car DVR and surveillance systems integrates the 3rd-generation CEVA-MM3101 imaging & vision DSP.
By integrating CEVA-XM4 as a dedicated vision processor in their next-generation SoC designs, Novatek and its customers can deploy vision algorithms to enable advanced applications such as surveillance systems with face detection and authentication, drone anti-collision systems and advanced driver assistance systems (ADAS). These types of applications are built utilizing CEVA's Deep Neural Network (CDNN2), a software framework that enables deep learning tasks to run on the CEVA-XM4 and outperform any GPU or CPU-based system in terms of speed, power consumption and memory bandwidth requirements.
CEVA's imaging and vision DSPs address the processing requirements of computational photography and computer vision applications such as video analytics, augmented reality and advanced driver assistance systems (ADAS). By offloading these tasks from the CPUs and GPUs, the DSP reduces the power consumption of the overall system while providing flexibility. The platform includes a vector processor developed specifically to deal with the complexities of such applications and an extensive Application Development Kit (ADK) to enable easy development environment. The CEVA ADK includes CEVA-Link, which streamlines software development and integration effort, a set of advanced software development tools and a range of software products and libraries optimized for the DSP. For embedded systems targeting deep learning, the CEVA Deep Neural Network (CDNN2) real-time neural network software framework streamlines machine learning deployment at a fraction of the power consumption of the leading GPU-based systems.
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