NXP Launches Deep Learning for Automotives at 30X Performance
With functions such as sensor fusion and driver replacement NXP’s deep learning toolkit can be embedded into automotives with less friction.
NXP Semiconductors (NASDAQ:NXPI) has announced that its eIQ Auto deep learning toolkit has broadened its functionalities to include enhanced driver monitoring, sensor fusion and driver replacement. The company says that its deep learning framework has 30 times higher performance than other deep learning models, reducing the friction involved in installation.
As quoted in the press release:
Deep Learning holds the promise of delivering better accuracy and better maintainability in object detection and classification over “traditional” computer vision algorithms, but the barriers to full automotive implementation bring complexity and steep costs.
The eIQ Auto toolkit aims to help customers reduce time to market by lowering the investment costs required to select and program embedded compute engines for each layer of a deep learning algorithm. The automated selection process leads to 30 times higher performance for given models compared to other embedded deep learning frameworks. This performance is achieved by optimizing the use of available resources and reducing time and development effort1. These dividends allow developers to evaluate, fine tune and deploy their applications for maximized overall performance.
Compliance with automotive-grade development standards and Functional Safety requirements are key benefits of eIQ Auto and S32V integration. eIQ Auto’s inference engine was developed in accordance with stringent requirements and is Automotive SPICE® compliant. The S32V processors offer the highest levels of functional safety supporting ISO 26262 up to ASIL-C, IEC 61508, and DO 178.
