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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

Is AI Improving A Broken Process?


Verification is fundamentally comparing two models, each derived independently, to find out if there are any different behaviors expressed between the two models. One of those models represents the intended design, and the other is part of the testbench. In an ideal flow, the design model would be derived from the specification, and each stage of the design process would be adding other deta... » read more

Improving PPA With AI


AI/ML/DL is starting to show up in EDA tools for a variety of steps in the semiconductor design flow, many of them aimed at improving performance, reducing power, and speeding time to market by catching errors that humans might overlook. It's unlikely that complex SoCs, or heterogeneous integration in advanced packages, ever will be perfect at first silicon. Still, the number of common error... » read more

Survey: 2022 Deep Learning Applications


The 2022 member list of deep learning projects and products that eBeam members are working on in photomask to wafer semiconductor manufacturing. Participating companies include Advantest, ASML, Canon, CEA-LETI, D2S, Fraunhofer IPMS, Hitachi High-Tech Corporation, imec, NuFlare Technology, Siemens Industries Software, Inc.; Siemens EDA, STMicroelectronics, and TASMIT. Click here to see the su... » read more

Image Processing For Vision AI


Recent years have seen an increasing need for Vision AI applications using AI to enable real-time image recognition. Vision AI, which substitutes AI for human visual recognition, requires optimal image processing. Renesas has released RZ/V2M as mid-class, and RZ/V2L as an entry class, Vision AI microprocessors (MPUs). Both products are equipped with DRP-AI which is Dynamically Reconfigurable Pr... » read more

Enhancing Datasets For Artificial Intelligence Through Model-Based Methods


By Dirk Mayer and Ulf Wetzker Industrial plants and processes are now digitized and networked, and AI can be used to evaluate the data generated by those facilities to increase productivity and quality. Machine learning (ML) methods can be applied to: Product quality classification in complex production processes. Condition monitoring of technical systems, which is used, for examp... » read more

AI Everywhere: Accelerating Chip Design At Every Node


Over the last few years, artificial Intelligence (AI) has increasingly played a significant role in the chip development process. But, when people talk about AI-designed chips, it is usually in the context of the latest, cutting-edge designs manufactured at advanced process nodes (7/5nm and smaller) and for good reason. Such designs constantly push the bounds of power, performance, and area (PP... » read more

Flexible USB4-Based Interface IP Solution For AI At The Edge


Consumers have become accustomed to smart devices that are powered by advances in artificial intelligence (AI). To expand the devices’ total addressable market, innovative device designers build edge AI accelerators and edge AI SoCs that support multiple use cases and integration options. This white paper describes a flexible USB4-based IP solution for edge AI accelerators and SoCs. The IP so... » read more

Amdahl Limits On AI


Software and hardware both place limits on how fast an application can run, but finding and eliminating the limitations is becoming more important in this age of multicore heterogeneous processing. The problem is certainly not new. Gene Amdahl (1922-2015) recognized the issue and published a paper about it in 1967. It provided the theoretical speedup for a defined task that could be expected... » read more

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