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Artificial Intelligence
Artificial intelligence – more specifically, the machine learning (ML) subset of AI – has a number of privacy problems.
Not only does ML require vast amounts of data for the training process, but the derived system is also provided with access to even greater volumes of data as part of the inference processing while in operation. … Read More
The potential for AI in cars, whether for driver assistance or full autonomy, has been trumpeted everywhere and continues to grow. Within the car we have vision, radar and ultrasonic sensors to detect obstacles in front, behind and to the side of the car. Outside the car, V2x promises to share real-time information between vehicles… Read More
The entire field of Artificial Intelligence (AI) has resulted from what is called “first principles thinking”, where problems are re-examined using a complete reassessment of the underlying issues and potential solutions. It is a testament to how effective this can be that AI is being used for a rapidly expanding number of applications… Read More
Block floating point (BFP) has been around for a while but is just now starting to be seen as a very useful technique for performing machine learning operations. It’s worth pointing out up front that bfloat is not the same thing. BFP combines the efficiency of fixed point operations and also offers the dynamic range of full floating… Read More
Ron Lowman, product marketing manager at Synopsys, recently posted an interesting technical bulletin on the Synopsys website entitled How AI in Edge Computing Drives 5G and the IoT. There’s been a lot of discussion recently about the emerging processing hierarchy of edge devices (think cell phone or self-driving car), cloud… Read More
Custom AI acceleration continues to gather steam. In the cloud, Alibaba has launched its own custom accelerator, following Amazon and Google. Facebook is in the game too and Microsoft has a significant stake in Graphcore. Intel/Mobileye have a strong lock on edge AI in cars and wireless infrastructure builders are adding AI capabilities… Read More
I wrote recently on ANSYS and TSMC’s joint work on thermal reliability workflows, as these become much more important in advanced processes and packaging. Xilinx provided their own perspective on thermal reliability analysis for their unquestionably large systems – SoC, memory, SERDES and high-speed I/O – stacked within a … Read More
Machine Learning (ML) has become extremely important for many computing applications, especially ones that involve interacting with the physical world. Along with this trend has come the development of many specialized ML processors for cloud and mobile applications. These chips work fine in the cloud or even in cars or phones,… Read More
This blog is the next in a series in which Paul Cunningham (GM of the Verification Group at Cadence), Jim Hogan and I pick a paper on a novel idea in verification and debate its strengths and opportunities for improvement.
Our goal is to support and appreciate further innovation in this area. Please let us know what you think and please… Read More
I attended a session on 2.5D silicon interposer analysis at DesignCon 2020. Like many presentations at this show, ecosystem collaboration was a focus. In this session, Jinsong Hu (principal application engineer at Cadence) and Yongsong He (senior staff engineer at Enflame Tech) presented approaches for interposer power modeling… Read More
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Rapidus, IBM, and the Billion-Dollar Silicon Sovereignty Bet