September 2018 - Semiconductor Engineering

Week in Review: IoT, Security, Auto
By Jeff Dorsch - 28 Sep, 2018 - Comments: 0

Internet of Things IBM this week launched the Watson Decision Platform for Agriculture, which combines artificial intelligence, Internet of Things technology, and cloud-based offerings, providing insights to farmers through a managed service. Among other features, growers can deploy drones to send photos to the IBM Cloud for AI-based trend analysis and detection of crop diseases. The platform ... » read more

Week In Review: Manufacturing, Test
By Mark LaPedus - 28 Sep, 2018 - Comments: 0

Tariffs The Federal Communications Commission (FCC) took a step in its ongoing efforts to remove regulatory barriers that inhibit the deployment of infrastructure necessary for 5G and other advanced wireless services in the U.S. "5G networks in America are key for powering the next generation of innovation, such as artificial intelligence, the Internet of Things and smart cities. (The FCC�... » read more

Planning Out Verification
By Ed Sperling - 28 Sep, 2018 - Comments: 0

OneSpin Solutions’ Nicolae Tusinschi talks with Semiconductor Engineering about how to move from specification to signoff in a verification flow. https://youtu.be/2zrgaq2I1SQ » read more

Week In Review: Design, Low Power
By Jesse Allen - 28 Sep, 2018 - Comments: 0

Arm announced a new processor targeted at autonomous driving applications. The Cortex-A76AE is a superscalar, out-of-order processor that incorporates Split-Lock safety technology. Split-Lock allows CPU clusters in an a SoC to be configured either in ‘split mode’ for high performance, allowing two (or four) independent CPUs in the cluster to be used for diverse tasks and applications, or �... » read more

Thermal Impact On Reliability At 7/5nm
By Ed Sperling - 28 Sep, 2018 - Comments: 0

Haroon Chaudhri, director of RedHawk Analysis Fusion at Synopsys, talks about why thermal analysis is shifting left in the design cycle and why this is so critical at the most advanced process nodes. https://youtu.be/wjkrEFLb2vY » read more

What Will Intel Do Next?
By Ed Sperling - 27 Sep, 2018 - Comments: 2

The writing is on the wall for big processor makers. Apple, Amazon, Facebook and Google are developing their own processors. In addition, there are more than 30 startups developing various types of AI accelerators, as well as a field of embedded FPGA vendors, a couple of discrete FPGA makers, and a slew of soft processor cores. This certainly hasn't been lost on Intel. As the world's largest... » read more

The Perfect Risk
By Brian Bailey - 27 Sep, 2018 - Comments: 0

The development of semiconductors is an act of risk management. Very simply put, if you take on too much risk, it could lead to product failure or a missed market window, both of which can cost $M. For a company that only produces one or two products a year, that can spell total disaster. If you do not take on enough risk, you are probably not going to end up with a competitive product that ... » read more

A Paradigm Shift With Vertical Nanowire FETs For 5nm And Beyond
By Taigon Song - 27 Sep, 2018 - Comments: 1

When I was in undergrad not so long ago, all my circuits and semiconductor textbooks/professors were talking about MOSFETs (metal-oxide semiconductor field-effect transistor) that were just “better” than BJTs (bi-polar junction transistor). There were still some old professors talking about how they did an excellent job using BJTs, but everyone knew it was MOSFET that was leading the game i... » read more

Next-Generation Liberty Verification And Debugging
By WeiLii Tan - 27 Sep, 2018 - Comments: 1

Accurate library characterization is a crucial step for modern chip design and verification. For full-chip designs with billions of transistors, timing sign-off through simulation is unfeasible due to run-time and memory constraints. Instead, a scalable methodology using static timing analysis (STA) is required. This methodology uses the Liberty file to encapsulate library characteristics such ... » read more

AI Chips Must Get The Floating-Point Math Right
By Sergio Marchese - 27 Sep, 2018 - Comments: 0

Most AI chips and hardware accelerators that power machine learning (ML) and deep learning (DL) applications include floating-point units (FPUs). Algorithms used in neural networks today are often based on operations that use multiplication and addition of floating-point values, which subsequently need to be scaled to different sizes and for different needs. Modern FPGAs such as Intel Arria-10 ... » read more

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