Current and Emerging Heterogeneous Integration Technologies For High-Performance Systems (Georgia Tech)


A technical paper titled "Heterogeneous Integration Technologies for Artificial Intelligence Applications" was published by Georgia Tech. Abstract "The rapid advancement of artificial intelligence (AI) has been enabled by semiconductor-based electronics. However, the conventional methods of transistor scaling are not enough to meet the exponential demand for computing power driven by AI. ... » read more

The Impact Of Simulation On The Carbon Footprint of Wafer Fab Equipment R&D


A new technical paper titled "Achieving Sustainability in the Semiconductor Industry: The Impact of Simulation and AI" was published by researchers at Lam Research. Abstract "Computational simulation has been used in the semiconductor industry since the 1950s to provide engineers and managers with a faster, more cost-effective method of designing semiconductors. With increased pressure in t... » read more

Simplifying AI Deployment from the Cloud to Edge and Endpoint


Artificial Intelligence (AI) is transforming every aspect of life. It is enhancing quality in industrial applications, enabling smart home systems, monitoring our safety as we work and play. Advances in technology have allowed us to run complex machine learning algorithms to tackle unique problems allowing those to be implemented also on embedded devices used in our daily life in home and indus... » read more

Security For AI And AI For Security


The aspect for AI was drastically changed after the introduction of ChatGPT from 2022. Even during 2010s, the question of whether the evolution of AI can overcome human’s logical thinking had been researched and developed (e.g., IBM Watson and Google AlphaGo). Now, a few years later from these results, everyone can experience the future potential of AI from the advent of generative AI. Major ... » read more

AI Takes Aim At Chip Industry Workforce Training


When all the planned fabs become operational, the semiconductor industry is likely to face a worker shortage of 100,000 each in the U.S. and Europe, and more than 200,000 in Asia-Pacific, according to a McKinsey report. Since the dawn of technology, people have worried that robots, automation, and AI will steal their jobs, but these tools also can be put to use to help fill the chip industry ta... » read more

Cadence Cerebrus In SaaS And Imagination Technologies Case Study


Artificial Intelligence (AI) has made noteworthy progress and is now ready and available for electronic design automation. The Cadence Cerebrus Intelligent Chip Explorer utilizes AI—specifically, reinforcement machine learning (ML) technology—combined with the industry-leading Cadence digital full flow to deliver better power, performance, and area (PPA) more quickly. However, this highl... » read more

The Data Crisis Is Unfolding — Are We Ready?


The rapid advancement of technology has led to an unprecedented amount of data being generated, captured, and consumed globally. However, this reliance on data comes at a considerable cost. The widespread sharing and processing of data is necessary to navigate our everyday lives. Still, any disruption to this process can have severe consequences, threatening our ability to function as a society... » read more

Thanks For The Memories!


“I want to maximize the MAC count in my AI/ML accelerator block because the TOPs rating is what sells, but I need to cut back on memory to save cost,” said no successful chip designer, ever. Emphasis on “successful” in the above quote. It’s not a purely hypothetical quotation. We’ve heard it many times. Chip architects — or their marketing teams — try to squeeze as much brag-... » read more

AI Races To The Edge


AI is becoming increasingly sophisticated and pervasive at the edge, pushing into new application areas and even taking on some of the algorithm training that has been done almost exclusively in large data centers using massive sets of data. There are several key changes behind this shift. The first involves new chip architectures that are focused on processing, moving, and storing data more... » 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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