tag: GPU

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

RISC-V Inches Toward The Center
By Ann Mutschler - 01 Oct, 2018 - Comments: 1

RISC-V is pushing further into the mainstream, showing up across a wide swath of designs and garnering support from a long and still-growing list of chipmakers, tools vendors, universities and foundries. In most cases it is being used as a complementary processor than a replacement for something else, but that could change in the future. What makes RISC-V particularly attractive to chipmaker... » read more

Machine Learning Shifts More Work to FPGAs, SoCs
By Kevin Fogarty - 24 Sep, 2018 - Comments: 0

A wave of machine-learning-optimized chips is expected to begin shipping in the next few months, but it will take time before data centers decide whether these new accelerators are worth adopting and whether they actually live up to claims of big gains in performance. There are numerous reports that silicon custom-designed for machine learning will deliver 100X the performance of current opt... » read more

Processing In Memory
By Ed Sperling - 05 Sep, 2018 - Comments: 1

Adding processing directly into memory is getting a serious look, particularly for applications where the volume of data is so large that moving it back and forth between various memories and processors requires too much energy and time. The idea of inserting processors into memory has cropped up intermittently over the past decade as a possible future direction, but it was dismissed as an e... » read more

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