tag: GPU

Using Memory Differently
By Ann Mutschler - 31 Jan, 2019 - Comments: 0

Chip architects are beginning to rewrite the rules on how to choose, configure and use different types of memory, particularly for chips with AI and some advanced SoCs. Chipmakers now have a number of options and tradeoffs to consider when choosing memories, based on factors such as the application and the characteristics of the memory workload, because different memory types work better tha... » read more

AI Market Ramps Everywhere
By Brian Bailey - 20 Dec, 2018 - Comments: 0

Artificial Intelligence (AI) has inspired the general populace, but its rapid rise over the past few years has given many people pause. From realistic concerns about robots taking over jobs to sci-fi scares about robots more intelligent than humans building ever smarter robots themselves, AI inspires plenty of angst. Within the technology industry, we have a better understanding about the pote... » read more

Fab Equipment Challenges For 2019
By Mark LaPedus - 17 Dec, 2018 - Comments: 0

After a period of record growth, the semiconductor equipment industry is facing a slowdown in 2019, in addition to several technical challenges that still need to be resolved. Generally, the equipment industry saw enormous demand in 2017, and the momentum extended into the first part of 2018. But then the memory market began deteriorating in the middle of this year, causing both DRAM and NAND ... » read more

The Cost Of Accuracy
By Brian Bailey - 13 Dec, 2018 - Comments: 0

How accurate does a system need to be, and what are you willing to pay for that accuracy? There are many sources of inaccuracy throughout the development flow of electronic systems, most of which involve complex tradeoffs. Inaccuracy leaves an impact on your design in ways you are not even aware of, hidden by best practices or guard-banding. EDA tools also inject some inaccuracy. As the i... » read more

FPGA Graduates To First-Tier Status
By Ed Sperling - 06 Dec, 2018 - Comments: 0

Robert Blake, president and CEO of Achronix, sat down with Semiconductor Engineering to talk about fundamental shifts in compute architectures and why AI, machine learning and various vertical applications are driving demand for discrete and embedded FPGAs. SE: What’s changing in the FPGA market? Blake: Our big focus is developing the next-generation architecture. We started this projec... » read more

Making Sure A Heterogeneous Design Will Work
By Ed Sperling - 03 Dec, 2018 - Comments: 0

An explosion of various types of processors and localized memories on a chip or in a package is making it much more difficult to verify and test these devices, and to sign off with confidence. In addition to timing and clock domain crossing issues, which are becoming much more difficult to deal with in complex chips, some of the new devices are including AI, machine learning or deep learning... » read more

AI Chip Architectures Race To The Edge
By Kevin Fogarty - 28 Nov, 2018 - Comments: 0

As machine-learning apps start showing up in endpoint devices and along the network edge of the IoT, the accelerators that make AI possible may look more like FPGA and SoC modules than current data-center-bound chips from Intel or Nvidia. Artificial intelligence and machine learning need powerful chips for computing answers (inference) from large data sets (training). Most AI chips—both tr... » read more

Machine Learning Moves Into Fab And Mask Shop
By Mark LaPedus - 14 Nov, 2018 - Comments: 0

Semiconductor Engineering sat down to discuss artificial intelligence (AI), machine learning, and chip and photomask manufacturing technologies with Aki Fujimura, chief executive of D2S; Jerry Chen, business and ecosystem development manager at Nvidia; Noriaki Nakayamada, senior technologist at NuFlare; and Mikael Wahlsten, director and product area manager at Mycronic. What follows are excerpt... » read more

What Makes A Good AI Accelerator
By Ann Mutschler - 25 Oct, 2018 - Comments: 3

The rapid growth and dynamic nature of AI and machine learning algorithms is sparking a rush to develop accelerators that can be optimized for different types of data. Where one general-purpose processor was considered sufficient in the past, there are now dozens vying for a slice of the market. As with any optimized system, architecting an accelerator — which is now the main processing en... » read more

Machine Learning Invades IC Production
By Mark LaPedus - 18 Oct, 2018 - Comments: 0

Semiconductor Engineering sat down to discuss artificial intelligence (AI), machine learning, and chip and photomask manufacturing technologies with Aki Fujimura, chief executive of D2S; Jerry Chen, business and ecosystem development manager at Nvidia; Noriaki Nakayamada, senior technologist at NuFlare; and Mikael Wahlsten, director and product area manager at Mycronic. What follows are excerpt... » read more

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