tag: LLMs

Microservice-Based LLM Agents Enable EDA Flow Automation (Duke Univ. and Univ. of Maryland)
By Technical Paper Link - 15 Aug, 2025 - Comments: 0

A new technical paper titled "AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents" was published by researchers at Duke University and University of Maryland. Abstract "Modern Electronic Design Automation (EDA) workflows, especially the RTL-to-GDSII flow, require heavily manual scripting and demonstrate a multitude of tool-specific interactions which limits scalabili... » read more

AI, From A To Z
By Ed Sperling - 15 Aug, 2025 - Comments: 0

First in a seven-part series: What's the difference between AI, ML, DL, LLMs, and agentic AI? Is it truly revolutionary, or is it an evolutionary series of steps that have enabled machines to do much more than in the past? Jon Herlocker, vice president and general manager of software analytics at Cohu, talks about the evolution of AI over nearly 70 years, the chain of innovation that has enable... » read more

Reliable Training Data Paramount To AI Model Success
By Ann Mutschler - 14 Aug, 2025 - Comments: 0

AI systems are increasingly being integrated into safety- and mission-critical applications ranging from automotive to health care and industrial IoT, stepping up the need for training data that is reliable, secure, and which is generated from trusted sources. AI activity is growing exponentially, as everybody tries to figure out how to apply it to their domain, application, or workload. In ... » read more

What’s Different About HBM4
By Ed Sperling - 11 Aug, 2025 - Comments: 0

Memory bandwidth is limiting the flow of huge datasets that are needed to train AI models. There is much more data to process, store, and retrieve, but the speed at which that data moves through high-bandwidth memory (HBM) stacks is significantly lower than the speed at which data can be processed. Frank Ferro, group director for product management at Cadence, talks about the new HBM4 standard,... » read more

Security Tradeoffs: A Difficult Balance
By Ann Mutschler - 06 Aug, 2025 - Comments: 0

Experts At The Table: Semiconductor Engineering sat down to discuss hardware security challenges, including new threat models from AI-based attacks, with Nicole Fern, principal security analyst at Keysight; Serge Leef, AI-For-Silicon strategist at Microsoft; Scott Best, senior director for silicon security products at Rambus; Lee Harrison, director of Tessent Automotive IC Solutions at Sieme... » read more

Largest High-Quality Verilog Dataset for LLM Fine-Tuning (Univ. of Florida)
By Technical Paper Link - 22 Jul, 2025 - Comments: 0

A new technical paper titled "VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation" was published by researchers at the University of Florida. Abstract "Large Language Models (LLMs) are gaining popularity for hardware design automation, particularly through Register Transfer Level (RTL) code generation. In this work, we examine the curr... » read more

AI In The IC Equipment Ecosystem
By Ed Sperling - 21 Jul, 2025 - Comments: 0

AI is playing an increasingly critical role in improving semiconductor equipment and processes, which are necessary as the industry moves to advanced manufacturing processes. This requires more steps, tighter integration and analysis of those various steps, and better optimization of tools. David Fried, corporate vice president at Lam Research, talks about how to accelerate the development of A... » read more

Transformers At The Edge: Efficient LLM Deployment
By Paul Karazuba - 17 Jul, 2025 - Comments: 0

Since the groundbreaking 2017 publication of “Attention Is All You Need,” the transformer architecture has fundamentally reshaped artificial intelligence research and development. This innovation laid the foundation for Large Language Models (LLMs) and Video Language Models (VLMs), fueling a wave of productization across the industry. A defining milestone was the public launch of ChatGPT in... » read more

Co-Designing Data Center Architecture To Support LLMs (Intel, Georgia Tech)
By Technical Paper Link - 14 Jul, 2025 - Comments: 0

A new technical paper titled "Scaling Intelligence: Designing Data Centers for Next-Gen Language Models" was published by Intel Corporation and Georgia Tech. An excerpt from the paper's abstract: "Our work provides a comprehensive co-design framework that jointly explores FLOPS, HBM bandwidth and capacity, multiple network topologies (two-tier vs. FullFlat optical), the size of the scale-ou... » read more

Shrinking LLMs With Self-Compression
By Jakub Przybyl - 08 Jul, 2025 - Comments: 0

Language models are becoming ever larger, making on-device inference slow and energy-intensive. A direct and surprisingly effective remedy is to prune complete channels whose contribution to the task is negligible. Our earlier work introduced a training-time procedure – Self-Compression [1, 4] – that lets back-propagation decide the bit-width of every channel, so unhelpful ones fade away. T... » read more

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