Artificial intelligence (AI) and machine learning (ML) are hot topics. Beyond the impact these technologies are having on the world around us, they are also having impact on the semiconductor and EDA ecosystem. I posted a blog last week that discussed how Cadence views AI/ML, both from a tool and ecosystem perspective. The is one… Read More
Artificial Intelligence
Machine Learning for EDA – Inside, Outside and Everywhere Else
Artificial intelligence (AI) is everywhere. The rise of the machines is upon us in case you haven’t noticed. Machine learning (ML) and its associated inference abilities promise to revolutionize everything from driving your car to making breakfast. We hear a lot about the macro, end-product impact of this technology, but there… Read More
There is No Easy Fix to AI Privacy Problems
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
Trends in AI and Safety for Cars
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
Mentor Helps Mythic Implement Analog Approach to AI
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
Webinar – FPGA Native Block Floating Point for Optimizing AI/ML Workloads
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
Edge Computing – The Critical Middle Ground
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
High-Level Synthesis at the Edge
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
Thermal Reliability Challenges in Automotive and Data Center Applications – A Xilinx Perspective
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
TinyML Makes Big Impact in Edge AI Applications
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
ASML- Soft revenues & Orders – But…China 49% – Memory Improving