Deep Learning (DL) Applications In Photomask To Wafer Semiconductor Manufacturing


Published by the eBeam Initiative Member Companies (February 2023), this list of artificial intelligence (AI) systems used by member companies in their semiconductor manufacturing products shows progress. New examples of systems using AI include: image processing and parameter tuning in lithography tool mask metrology system B-SPline Control Point generation tool sem... » read more

Accelerate The Algorithm To Silicon Development With Stratus HLS


Growth in demand for artificial intelligence (AI) and digital signal processing (DSP) applications, coupled with advances in semiconductor process technology, drives increasingly denser SoCs. These complex SoCs further challenge the design team’s ability to meet performance, power, and area (PPA) goals within tight time-to-market windows. We need automated and targeted solutions that efficien... » read more

Improving Chip Efficiency, Reliability, And Adaptability


Peter Schneider, director of Fraunhofer Institute for Integrated Circuits' Engineering of Adaptive Systems Division, sat down with Semiconductor Engineering to talk about new models and approaches for ensuring the integrity and responsiveness of systems, and how this can be done within a given power budget and at various speeds. What follows are excerpts of that conversation. SE: Where are y... » read more

FP8: Cross-Industry Hardware Specification For AI Training And Inference (Arm, Intel, Nvidia)


Arm, Intel, and Nvidia proposed a specification for an 8-bit floating point (FP8) format that could provide a common interchangeable format that works for both AI training and inference and allow AI models to operate and perform consistently across hardware platforms. Find the technical paper titled " FP8 Formats For Deep Learning" here. Published Sept 2022. Abstract: "FP8 is a natural p... » read more

Artificial Intelligence 101: It’s Math, Not Magic


The term artificial intelligence (AI) can be somewhat misleading. While the medium of intelligence is designed (and, in that sense, artificial or human-made), the intelligence itself is based on very real data. However, most people hear “AI” and think of futuristic robots or scenes from science fiction movies, not recognizing that the origin of AI is not fictional or magical — it’s math... » read more

Speech Applications Will Enable A New Category Of Edge AI Chips


Speech recognition has become an increasingly important feature in a wide range of devices. Wakewords such as Alexa or OK Google or Siri have now become a standard feature of wearables, smart-speakers, mobile phones, and even laptops. These devices have already shipped in millions of units and consumers are getting better at utilizing this feature. The wakeword recognition feature is slowly evo... » read more

AI At The Edge: Optimizing AI Algorithms Without Sacrificing Accuracy


The ultimate measure of success for AI will be how much it increases productivity in our daily lives. However, the industry has huge challenges in evaluating progress. The vast number of AI applications is in constant churn: finding the right algorithm, optimizing the algorithm, and finding the right tools. In addition, complex hardware engineering is rapidly being updated with many different s... » read more

Brain-Inspired Computing Device That Programs/RePrograms HW On Demand With Electrical Pulses


Multiple academic and government institutions jointly developed a new computer device that can "program and program computer hardware on demand through electrical pulses," according to this Argonne National Lab news release. The device's key materials are neodymium, nickel and oxygen and is referred to as a perovskite nickelate. This new research paper titled "Reconfigurable perovskite nicke... » read more

Active Learning: Integrating Natural Intelligence Into Artificial Intelligence


Today, very few people would likely deny the fact that data can present major added value for companies. But analyzing data from production processes reveals the incompleteness of data collection and the associated reduced potential of the data that can be leveraged. Typical shortcomings include: Incomplete representation of processes in the dataspace, Inadequate connection of processes... » read more

AI-Powered Verification


With functional verification consuming more time and effort than design, the chip industry is looking at every possible way to make the verification process more effective and more efficient. Artificial intelligence (AI) and machine learning (ML) are being tested to see how big an impact they can have. While there is progress, it still appears to be just touching the periphery of the problem... » read more

← Older posts Newer posts →