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NXP Accelerates the Transformation to Software-Defined Vehicles (SDV) with Agreement to Acquire TTTech Auto
NXP Semiconductors N.V. (NASDAQ: NXPI), the worldwide leader in automotive processing and networking, announced today that it has entered into a definitive agreement to acquire TTTech Auto in an all-cash transaction valued at $625 million.
TTTech Auto, based in Vienna, Austria, is a leader in innovating unique safety-critical systems and middleware for software-defined vehicles (SDVs). TTTech Auto has established business relationships with many leading automotive OEMs, empowering them to focus on the driving experience while its solutions optimize performance, safety, integration, and software updates.
Pending regulatory approvals, TTTech Auto including its management team, intellectual property, assets, and approximately 1,100 engineering staff will join NXPs automotive team. As part of NXP, TTTech Auto will continue to serve existing customers and expand its global footprint under the NXP brand.
NXP to Acquire Automotive Networking Pioneer Aviva Links
NXP Semiconductors N.V. (NASDAQ: NXPI), the worldwide leader in automotive processing and networking, announced that it has entered into a definitive agreement to acquire Aviva Links , a provider of Automotive SerDes Alliance (ASA) compliant in-vehicle connectivity solutions in an all-cash transaction valued at $242.5 million.
Aviva Links brings the industry’s most advanced ASA compliant portfolio, supporting SerDes point-to-point (ASA-ML) and Ethernet-based connectivity (ASA-MLE) with data rates up to 16 Gbps. The company has secured design wins at two major automotive OEMs and is sampling its devices to various OEMs and Tier-1 suppliers.
Machine Learning Might Save Time on Chip Testing
Engineers at NXP have developed a machine-learning algorithm that learns the patterns of test results and figures out the subset of tests that are really needed and those that they could safely do without.
Shroff says the problem has certain similarities to the machine learning-based recommender systems used in e-commerce. “We took the concept from the retail world, where a data analyst can look at receipts and see what items people are buying together,” he says. “Instead of a transaction receipt, we have a unique part identifier and instead of the items that a consumer would purchase, we have a list of failing tests.”
Shroff and his colleagues analyzed data obtained from testing seven microcontrollers and applications processors built using advanced chipmaking processes. Depending on which chip was involved, they were subject to between 41 and 164 tests, and the algorithm was able to recommend removing 42 to 74 percent of those tests. Extending the analysis to data from other types of chips led to an even wider range of opportunities to trim testing.