tag: AI

How Can You Use ChatGPT For Software Testing?
By Jenn Mullen - 11 Jan, 2024 - Comments: 0

All eyes have been on OpenAI and its brainchild ChatGPT in recent months. ChatGPT’s ability to understand and respond to complex instructions and deliver detailed responses to user prompts has led to an explosive rise in its popularity with the public. If you were to search online for “ChatGPT tips,” you can find content to help you use the tool to tailor resumes to job postings, create... » read more

Glitch Power Issues Grow At Advanced Nodes
By Ann Mutschler - 11 Jan, 2024 - Comments: 0

An estimated 20% to 40% of total power is being wasted due to glitch in some of the most advanced and complex chip designs, and at this point there is no single best approach for how and when to address it, and mixed information about how effective those solutions can be. Glitch power is not a new phenomenon. DSP architects and design engineers are well-versed in the power wasted by long, sl... » read more

Scaling Server Memory Performance To Meet The Demands Of AI
By Tim Messegee - 11 Jan, 2024 - Comments: 0

AI, whether we’re talking about the number of parameters used in training or the size of large language models (LLMs), continues to grow at a breathtaking rate. For over a decade, we’ve witnessed a 10X per year scaling. It’s a growth rate that puts pressure on every aspect of the computing stack: processing, memory, networking, you name it. The platform vendors are responding to the in... » read more

Getting Optimal PPA For HPC & AI Applications With Foundation IP
By Andrew Appleby - 11 Jan, 2024 - Comments: 0

By Andrew Appleby, Xiaorui Hu, and Bhavana Chaurasia The demand for application-specific system-on-chips (SoCs) for compute applications is ever-increasing. Today, the diversity of requirements means there is a need for a rich set of compute solutions in a wide range of process technologies. The resulting products may have very different but demanding power, performance, and area (PPA) requi... » read more

Pressure Builds On Failure Analysis Labs
By Laura Peters - 09 Jan, 2024 - Comments: 0

Failure analysis labs are becoming more fab-like, offering higher accuracy in locating failures and accelerating time-to-market of new devices. These labs historically have been used for deconstructing devices that failed during field use, known as return material authorizations (RMAs), but their role is expanding. They now are becoming instrumental in achieving first silicon and ramping yie... » read more

Unlocking Value: The Power of AI in Semiconductor Test
By Advantest - 09 Jan, 2024 - Comments: 0

AI (Artificial Intelligence) and data analytics empower semiconductor manufacturers to extract valuable insights from the massive amounts of data generated throughout the silicon lifecycle. By leveraging AI algorithms, semiconductor manufacturers can optimize silicon design, assembly, and testing processes. Through the analysis of vast datasets, AI can identify patterns, predict failures, and o... » read more

SRAM’s Role In Emerging Memories
By Karen Heyman - 05 Jan, 2024 - Comments: 1

Experts at the Table — Part 3: Semiconductor Engineering sat down to talk about AI, the latest issues in SRAM, and the potential impact of new types of memory, with Tony Chan Carusone, CTO at Alphawave Semi; Steve Roddy, chief marketing officer at Quadric; and Jongsin Yun, memory technologist at Siemens EDA. What follows are excerpts of that conversation. Part one of this conversation can be ... » read more

2023: A Good Year For Semiconductors
By Brian Bailey - 21 Dec, 2023 - Comments: 0

Looking back, 2023 has had more than its fair share of surprises, but who were the winners and losers? The good news is that by the end of the year, almost everyone was happy. That is not how we exited 2022, where there was overcapacity, inventories had built up in many parts of the industry, and few sectors — apart from data centers — were seeing much growth. The supposed new leaders we... » read more

Fabs Begin Ramping Up Machine Learning
By Anne Meixner - 19 Dec, 2023 - Comments: 0

Fabs are beginning to deploy machine learning models to drill deep into complex processes, leveraging both vast compute power and significant advances in ML. All of this is necessary as dimensions shrink and complexity increases with new materials and structures, processes, and packaging options, and as demand for reliability increases. Building robust models requires training the algorithms... » read more

Data Formats For Inference On The Edge
By Brian Bailey - 14 Dec, 2023 - Comments: 0

AI/ML training traditionally has been performed using floating point data formats, primarily because that is what was available. But this usually isn't a viable option for inference on the edge, where more compact data formats are needed to reduce area and power. Compact data formats use less space, which is important in edge devices, but the bigger concern is the power needed to move around... » read more

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