How Ultra Ethernet And UALink Enable High-Performance, Scalable AI Networks


By Ron Lowman and Jon Ames AI workloads are significantly driving innovation in the interface IP market. The exponential increase in AI model parameters, doubling approximately every 4-6 months, stands in stark contrast to the slower pace of hardware advancements dictated by Moore's Law, which follows an 18-month cycle. This discrepancy demands hardware innovations to support AI workloads, c... » read more

Choosing The Right Memory Solution For AI Accelerators


To meet the increasing demands of AI workloads, memory solutions must deliver ever-increasing performance in bandwidth, capacity, and efficiency. From the training of massive large language models (LLMs) to efficient inference on endpoint devices, choosing the right memory technology is critical for chip designers. This blog explores three leading memory solutions—HBM, LPDDR, and GDDR—and t... » read more

MACs Are Not Enough: Why “Offload” Fails


For the past half-decade, countless chip designers have approached the challenges of on-device machine learning inference with the simple idea of building a “MAC accelerator” – an array of high-performance multiply-accumulate circuits – paired with a legacy programmable core to tackle the ML inference compute problem. There are literally dozens of lookalike architectures in the market t... » read more

2025: So Many Possibilities


The stage is set for a year of innovation in the chip industry, unlike anything seen for decades, but what makes this period of advancement truly unique is the need to focus on physics and real design skills. Planar scaling of SoCs enabled design and verification tools and methodologies to mature on a relatively linear path, but the last few years have created an environment for more radical... » read more

What’s The Best Way To Sell An Inference Engine?


The burgeoning AI market has seen innumerable startups funded on the strength of their ideas about building faster, lower-power, and/or lower-cost AI inference engines. Part of the go-to-market dynamic has involved deciding whether to offer a chip or IP — with some newcomers pivoting between chip and IP implementations of their ideas. The fact that some companies choose to sell chips while... » read more

AI Won’t Replace Subject Matter Experts


Experts at The Table: The emergence of LLMs and other forms of AI has sent ripples through a number of industries, raising fears that many jobs could be on the chopping block, to be replaced by automation. Whether that’s the case in semiconductors, where machine learning has become an integral part of the design process, remains to be seen. Semiconductor Engineering sat down with a panel of e... » read more

Using Test And Metrology Data For Dynamic Process Control


Advanced packaging is transforming semiconductor manufacturing into a multi-dimensional challenge, blending 2D front-end wafer fabrication with 2.5D/3D assemblies, high-frequency device characterization, and complex yield optimization strategies. These combinations are essential to improving performance and functionality, but they create some thorny issues for which there are no easy fixes. ... » read more

Semiconductor Manufacturing’s Transformational Challenges


Semiconductor manufacturing is going through massive transformational challenges driven by strong demand for advanced computing, fueled by AI, cloud, the electrification of the economy, and the need for compute power in data centers to support these applications. With the slowdown of Moore’s Law, more compute power will not be achieved by just increasing transistor density. Not only is Moo... » read more

Do More With Less In Semiconductor Manufacturing


The recent resolution of labor disputes sheds light on a universal concern: the balance between automation and workforce dynamics. These situations mirror a challenge faced in semiconductor manufacturing—embracing AI without displacing the people driving the industry. Moving beyond automation fears US port workers expressed concerns about automation technologies, such as autonomous cranes a... » read more

Accelerating The AI Economy Through Heterogeneous Integration


The world is rapidly transitioning from an internet economy to an AI economy. In the internet economy, we stayed constantly connected to the internet 24/7 through our smartphones, PCs, and IoT devices. However, in the AI economy, every aspect of our lives is interwoven with artificial intelligence. You may already be familiar with AI tools such as ChatGPT or Google Gemini, which answer question... » read more

← Older posts