Chip Industry Week in Review


China's Xiaomi debuted an in-house-designed 10-core mobile SoC built on a 3nm process. The company did not identify the foundry. It also announced plans to invest 50 billion yuan (~$7B) over the next decade to develop high-end smartphone chips, as part of a 200 billion yuan (~$28B) core technology investment over the next five years. Kearney and SEMI released a report assessing the state of ... » read more

Future-proofing AI Models


Experts At The Table: Making sure AI accelerators can be updated for future requirements is becoming essential due to the rapid introduction of new models. Semiconductor Engineering sat down to discuss the challenges of future-proofing these designs with Marc Meunier, director of ecosystem development at Arm; Jason Lawley, director of product marketing for AI IP at Cadence; Paul Karazuba, vic... » read more

AI Accelerators Moving Out From Data Centers


Experts At The Table: The explosion in AI data is driving chipmakers to look beyond a single planar SoC. Semiconductor Engineering sat down to discuss the need for more computing and the expanding role of chiplets with Marc Meunier, director of ecosystem development at Arm; Jason Lawley, director of product marketing for AI IP at Cadence; Paul Karazuba, vice president of marketing at Expedera; ... » read more

Impact of AI On IP And Chip Design


By Global Semiconductor Alliance (GSA) In conjunction with the Global Semiconductor Alliance's IP Interest group, Expedera explores the impact of AI on intellectual property (IP) and Chip Design, providing comprehensive details and multifaceted data to cover all aspects of the semiconductor industry. It highlights AI growth trends, market predictions, and current silicon chip design innovati... » read more

AI Drives Re-Engineering Of Nearly Everything In Chips


AI's ability to mine patterns across massive quantities of data is causing fundamental changes in how chips are used, how they are designed, and how they are packaged and built. These shifts are especially apparent in high-performance AI architectures being used inside of large data centers, where chiplets are being deployed to process, move, and store massive amounts of data. But they also ... » read more

Chip Industry Week In Review


Don't have time to read this? Check out Semiconductor Engineering's Inside Chips podcast.  The U.S. Department of Commerce is investigating TSMC for potential export control violations involving Huawei chips, reports Reuters. The probe follows TechInsights' teardown of a Huawei AI accelerator chip last year. The foundry, meanwhile, maintains it has not shipped any chips to Huawei since 2020... » read more

Implementing AI Activation Functions


Activation functions play a critical role in AI inference, helping to ferret out nonlinear behaviors in AI models. This makes them an integral part of any neural network, but nonlinear functions can be fussy to build in silicon. Is it better to have a CPU calculate them? Should hardware function units be laid down to execute them? Or would a lookup table (LUT) suffice? Most architectures inc... » read more

Transforming The Semiconductor Industry: Future Roadmap For Generative AI On The Edge


In the third of a three-part series, Expedera, in conjunction with the Global Semiconductor Alliance’s Emerging Technologies (EmTech) group explores “Transforming the Semiconductor Industry: Future Roadmap for Generative AI on the Edge”. This white paper explores key applications for Generative AI and the features they enable, as well as an examination of how model growth and differing mo... » read more

Unlocking Generative AI On The Edge Across The Semiconductor Value Chain


In the second of a three-part series, Expedera, in conjunction with the Global Semiconductor Alliance’s Emerging Technologies (EmTech) group, explores “Unlocking Generative AI on the Edge across the Semiconductor Value Chain”. Included in this white paper is an examination of how members of the value chain (including IP providers, EDA vendors, fabless chip makers, foundries, OSATs, OEMs, ... » read more

Normalization Keeps AI Numbers In Check


AI training and inference are all about running data through models — typically to make some kind of decision. But the paths that the calculations take aren’t always straightforward, and as a model processes its inputs, those calculations may go astray. Normalization is a process that can keep data in bounds, improving both training and inference. Foregoing normalization can result in at... » read more

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