Generative AI On Mobile Is Running On The Arm CPU


By Adnan Al-Sinan and Gian Marco Iodice 2023 was the year that showcased an impressive number of use cases powered by generative AI. This disruptive form of artificial intelligence (AI) technology is at the heart OpenAI's ChatGPT and Google’s Gemini AI model, with it demonstrating the opportunity to simplify work and advance education through generating text, images, or even audio content ... » read more

Yield Tracking In RDL


Yield is a much bigger issue when it comes to panel-level packages, which may contain up to 24 RDL layers. Just finding the defects is a massive challenge, let alone understanding how they will impact the entire device. Many of these advanced packages are being used in data centers for generative AI, and killer defects caused by bridges and opens can cause serious problems. What happens, for in... » read more

Preparing For An AI-Driven Future In Chips


Experts at the Table: Semiconductor Engineering sat down to discuss the impact of AI on semiconductor architectures, tools, and security, with Michael Kurniawan, business strategy manager at Accenture; Kaushal Vora, senior director and head of business acceleration and ecosystem at Renesas Electronics; Paul Karazuba, vice president of marketing at Expedera; and Chowdary Yanamadala, technology s... » read more

Why There Are Still No Commercial 3D-ICs


Building chips in three dimensions is drawing increased attention and investment, but so far there have been no announcements about commercial 3D-IC chips. There are some fundamental problems that must be overcome and new tools that need to be developed. In contrast, the semiconductor industry is becoming fairly comfortable with 2.5D integration, where individual dies are assembled on some k... » read more

How Is The Chip Industry Really Doing?


Throughout 2023, the general consensus among chip industry watchers was that IC sales were flat to down, fueled by market saturation for smart phones and PCs and excess inventory and capacity in DRAM and flash. But that doesn't tell the whole story, which is becoming highly nuanced and complicated. Unlike in the past, understanding how the chip industry is faring is no longer a simple math f... » read more

Reducing Power In Data Centers


The rollout of generative AI, coupled with more data in general, is requiring data centers to run servers harder and longer. That, in turn, is generating more heat and accelerating aging, and to ensure these systems continue working over their projected lifetimes, chipmakers are building extra margin into chips. That increases the amount of energy required to run and cool them, and it can short... » read more

Modeling Compute In Memory With Biological Efficiency


The growing popularity of generative AI, which uses natural language to help users make sense of unstructured data, is forcing sweeping changes in how compute resources are designed and deployed. In a panel discussion on artificial intelligence at last week’s IEEE Electron Device Meeting, IBM’s Nicole Saulnier described it as a major breakthrough that should allow AI tools to assist huma... » read more

The Evolution Of Generative AI Up To The Model-Driven Era


Generative AI has become a buzzword in 2023 with the explosive proliferation of ChatGPT and large language models (LLMs). This brought about a debate about which is trained on the largest number of parameters. It also expanded awareness of the broader training of models for specific applications. Therefore, it is unsurprising that an association has developed between the term “Generative AI�... » read more

Considerations For Accelerating On-Device Stable Diffusion Models


One of the more powerful – and visually stunning – advances in generative AI has been the development of Stable Diffusion models. These models are used for image generation, image denoising, inpainting (reconstructing missing regions in an image), outpainting (generating new pixels that seamlessly extend an image's existing bounds), and bit diffusion. Stable Diffusion uses a type of dif... » read more

Applications Of Large Language Models For Industrial Chip Design (NVIDIA)


A technical paper titled “ChipNeMo: Domain-Adapted LLMs for Chip Design” was published by researchers at NVIDIA. Abstract: "ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, we instead adopt the following domain adaptation techniques: custom tokenizers, domain-ad... » read more

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