Week In Review: Auto, Security, Pervasive Computing


Automotive Cadence achieved ASIL Level B in support of D (ASIL B(D))-compliant certification for its Tensilica ConnX B10 and ConnX B20 DSPs, which are designed for automotive radar, lidar, and vehicle-to-everything (V2X). SGS-TÜV Saar certified that the DSPs have support for random hardware faults and systematic faults. Synopsys is acquiring Moortec, whose process, voltage, and temperature... » read more

Week In Review: Design, Low Power


Tools & IP Cadence debuted System-Level Verification IP (System VIP), a suite of tools and libraries for automating SoC testbench assembly, bus and CPU traffic generation, cache-coherency validation, and system performance bottleneck analysis. Tests created using the System VIP solution are portable across Cadence simulation, emulation and prototyping engines and can also be extended to po... » read more

Week In Review: Design, Low Power


Perforce Software acquired Methodics. Founded in 2006 and based in San Francisco, Methodics' IP lifecycle management and traceability software will join Perforce's larger portfolio of DevOps software that includes version control, Agile planning, and static code analysis. The two companies have had a strategic partnership in place with customers using software from both companies. Terms of the ... » read more

Week In Review: Auto, Security, Pervasive Computing


GPU maker Nvidia may be interested in a purchasing Arm, Bloomberg reports, if current owner Softbank, the Japanese investment group run by billionaire Masayoshi Son, is even selling the company. Softbank may have approached Apple to gauge interest, but Apple reportedly said no. The British-based Arm’s instruction set architecture IP dominates the mobile market, especially with Apple is switch... » read more

Startup Funding: February 2020


AI drew the biggest investments last month, with two AI hardware companies and one autonomous driving software startup pulling in nine-figure sums. Investors also pumped money into semiconductor manufacturing and test equipment, notably around EUV lithography and advanced packaging. AI Hardware SambaNova Systems received $250M in Series C funding for its software-defined hardware for AI, le... » read more

Thoroughly Verifying Complex SoCs


The number of things that can go wrong in complex SoCs targeted at leading-edge applications is staggering, and there is no indication that verifying these chips will function as expected is going to get any easier. Heterogeneous designs developed for leading-edge applications, such as 5G, IoT, automotive and AI, are now complex systems in their own right. But they also need to work in conju... » read more

Week In Review: Design, Low Power


Accellera formed the Universal Verification Methodology Analog/Mixed-Signal Working Group (UVM-AMS WG), which will work to develop a standard that will provide a unified analog/mixed-signal verification methodology based on UVM to improve the verification of AMS integrated circuits and systems. “Our objective is to standardize a method to drive and monitor analog/mixed-signal nets within UVM,... » read more

Week In Review: IoT, Security, Automotive


Automotive Porsche’s electric race car, the 99X Electric, used ANSYS Technology’s system-level simulation solutions to create an advanced electric powertrain. The powertrain is also being adapted for use in Porsche’s consumer electric cars. "ANSYS system-level simulations are instrumental for optimizing the Porsche E-Performance Powertrain's motor, gearbox, power electronics and control ... » read more

Memory Options And Tradeoffs


Steven Woo, Rambus fellow and distinguished inventor, talks with Semiconductor Engineering about different memory options, why some are better than others for certain tasks, and what the tradeoffs are between the different memory types and architectures.     Related Articles/Videos Memory Tradeoffs Intensify In AI, Automotive Applications Why choosing memories and archi... » read more

How To Manage DFT For AI Chips


Semiconductor companies are racing to develop AI-specific chips to meet the rapidly growing compute requirements for artificial intelligence (AI) systems. AI chips from companies like Graphcore and Mythic are ASICs based on the novel, massively parallel architectures that maximize data processing capabilities for AI workloads. Others, like Intel, Nvidia, and AMD, are optimizing existing archite... » read more

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