Verification In The Era Of Autonomous Driving, Artificial Intelligence And Machine Learning


The last couple of weeks have been busy with me participating on three panels that dealt with AI and machine learning in the contexts of automotive and aero/defense, in San Jose, Berlin and Detroit. The common theme? Data is indeed the new oil, and it messes with traditional value creation in electronics. Also, requirements for system design and verification are changing and there are completel... » read more

DRAM Scaling Challenges Grow


DRAM makers are pushing into the next phase of scaling, but they are facing several challenges as the memory technology approaches its physical limit. DRAM is used for main memory in systems, and today’s most advanced devices are based on roughly 18nm to 15nm processes. The physical limit for DRAM is somewhere around 10nm. There are efforts in R&D to extend the technology, and ultimate... » read more

Why Standard Memory Choices Are So Confusing


System architects increasingly are developing custom memory architectures based upon specific use cases, adding to the complexity of the design process even though the basic memory building blocks have been around for more than half a century. The number of tradeoffs has skyrocketed along with the volume of data. Memory bandwidth is now a gating factor for applications, and traditional memor... » read more

Making Sense Of Inferencing Options


Ian Bratt, fellow in Arm’s machine learning group, sheds light on all the different processing elements in machine learning, how different end user requirements affect those choices, why CPUs are a critical element in orchestrating what happens in these systems, and how power and software play into these choices. » read more

AI’s Impact On Power And Performance


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

Implementing Low-Power Machine Learning In Smart IoT Applications


By Pieter van der Wolf and Dmitry Zakharov Increasingly, machine learning (ML) is being used to build devices with advanced functionalities. These devices apply machine learning technology that has been trained to recognize certain complex patterns from data captured by one or more sensors, such as voice commands captured by a microphone, and then performs an appropriate action. For example,... » read more

Scalable Platforms For Evolving AI


Wear and tear on big, heavy vehicles such as trains can cause unexpected delays and repairs, not to mention create safety hazards that can go unnoticed for months until they become critical. In the past, maintenance teams personally examined the undercarriage of a locomotive to look for stress cracks and other anomalies. Later, imaging and sonar technologies were introduced to find what the hum... » read more

Using Machine Learning To Break Down Silos


Jeff David, vice president of AI solutions at PDF Solutions, talks with Semiconductor Engineering about where machine learning can be applied into semiconductor manufacturing, how it can be used to break down silos around different process steps, how active learning works with human input to tune algorithms, and why it’s important to be able to choose different different algorithms for differ... » read more

AI’s Blind Spots


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


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

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