tag: AI

GDDR6 Drilldown: Applications, Tradeoffs And Specs
By Ed Sperling - 15 Nov, 2019 - Comments: 0

Frank Ferro, senior director of product marketing for IP cores at Rambus, drills down on tradeoffs in choosing different DRAM versions, where GDDR6 fits into designs versus other types of DRAM, and how different memories are used in different vertical markets. » read more

AI’s Impact On Power And Performance
By Ed Sperling - 14 Nov, 2019 - Comments: 0

AI/ML is creeping into everything these days. There are AI chips, and there are chips that include elements of AI, particularly for inferencing. The big question is how well they will affect performance and power, and the answer isn't obvious. There are two main phases of AI, the training and the inferencing. Almost all training is done in the cloud using extremely large data sets. In fact, ... » read more

The Challenge Of Defining Worst Case
By Brian Bailey - 14 Nov, 2019 - Comments: 0

Worst case conditions within a chip are impossible to define. But what happens if you missed a corner case that causes chip failure? As the semiconductor market becomes increasingly competitive — startups and systems companies are now competing with established chipmakers — no one can afford to consider theoretical worst cases. Instead, they must intelligently prune the space to make sur... » read more

Speeding Up 3D Design
By Brian Bailey - 14 Nov, 2019 - Comments: 0

2.5D and 3D designs have garnered a lot of attention recently, but when should these solutions be considered and what are the dangers associated with them? Each new packaging option trades off one set of constraints and problems for a different set, and in some cases the gains may not be worth it. For other applications, they have no choice. The tooling in place today makes it possible to de... » read more

AI’s Blind Spots
By Ed Sperling - 12 Nov, 2019 - Comments: 1

The rush to utilize AI/ML in nearly everything and everywhere raises some serious questions about how all of this technology will evolve, age and perform over time. AI is very useful at doing certain tasks, notably finding patterns and relationships in broad data sets that are well beyond the capabilities of the human mind. This is very valuable for adding efficiency into processes of all so... » read more

Leveraging Data In Chipmaking
By Ed Sperling - 12 Nov, 2019 - Comments: 0

John Kibarian, president and CEO of PDF Solutions, sat down with Semiconductor Engineering to talk about the impact of data analytics on everything from yield and reliability to the inner structure of organizations, how the cloud and edge will work together, and where the big threats are in the future. SE: When did you recognize that data would be so critical to hardware design and manufact... » read more

Simplifying Silicon Bring-Up And Debug On ATE equipment With ATE-Connect
By Mentor, a Siemens Business - 11 Nov, 2019 - Comments: 0

The silicon bring-up process is ripe for improvement. Tessent SiliconInsight with ATE-Connect technology eliminates communication barriers between proprietary tester-specific software and DFT platforms, which accelerates debug of IJTAG devices, speeds product ramps, and reduces time-to-market for products in 5G wireless communications, autonomous driving, and artificial intelligence. Read mo... » read more

October ’19 Startup Funding: Mega Harvest
By Jeff Dorsch - 11 Nov, 2019 - Comments: 0

Seventeen startups took in mega-rounds of $100 million or more during October, with a cumulative total of just over $3.2 billion. Cybersecurity startups continued to be popular with private investors during the month of October, with 15 financing rounds. Twenty automotive and mobility technology firms picked up new investments. Analytics firms, artificial intelligence/machine learning techno... » read more

How 5G Affects Test
By Ed Sperling - 11 Nov, 2019 - Comments: 0

David Hall, head of semiconductor marketing at National Instruments, talks with Semiconductor Engineering about architectural changes to infrastructure due to the rollout of 5G and how the move from macrocells to small cells is changing test requirements.         Subscribe to Semiconductor Engineering's YouTube Channel here » read more

Modeling AI Inference Performance
By Geoff Tate - 07 Nov, 2019 - Comments: 0

The metric in AI Inference that matters to customers is either throughput/$ for their model and/or throughput/watts for their model. One might assume throughput will correlate with TOPS, but you’d be wrong. Examine the table below: The Nvidia Tesla T4 gets 7.4 inferences/TOP, Xavier AGX 15 and InferX 1 34.5. And InferX X1 does it with 1/10th to 1/20th of the DRAM bandwidth of the ... » read more

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