How And Why To Optimize NPUs


Experts At The Table: AI/ML are driving a steep ramp in neural processing unit (NPU) design activity for everything from data centers to edge devices such as PCs and smartphones.  Semiconductor Engineering sat down with Jason Lawley, director of product marketing, AI IP at Cadence; Sharad Chole, chief scientist and co-founder at Expedera; Steve Roddy, chief marketing officer at Quadric; Steven... » read more

Will 2026 Be Dominated By AI?


Many opportunities and problems became highly interlinked in 2025, fueled by the historic growth in everything AI. But how close are we coming to breaking points, and what are people doing to mitigate them? That is the story that will unfold this year. AI's penetration into an increasing number of workloads is placing almost quadratic demands on compute, memory, interconnect, and the archite... » read more

Challenges In Moving Data In Chips


The number of processes running simultaneously inside of chips is growing, fueled by massive increases in data from AI and sensors everywhere. The challenge now, particularly in multi-die assemblies, is how to prioritize where signals go, how quickly they move, and when they're supposed to arrive at shared memories. Andy Nightingale, vice president of product management and marketing at Arteris... » read more

Semiconductor Manufacturing In The AI Era


At the 2025 PDF Solutions Users Conference, CEO John Kibarian delivered a wide-ranging keynote that positioned the semiconductor industry at a pivotal inflection point, one driven by explosive AI demand but constrained by unprecedented manufacturing complexity. His central message: the path to a trillion-dollar semiconductor industry by 2030 requires fundamentally rethinking how manufacturers c... » read more

Automotive Outlook: 2026


The automotive industry stands at a crossroads entering 2026, facing a complex interplay of global tariffs, evolving electric vehicle (EV) dynamics, and the infusion of AI into just about everything. As manufacturers and suppliers navigate recent financing shifts and regulatory changes, they also must address consumer concerns over EV affordability and range, OEM concerns over when to develo... » read more

Software-Defined Hardware-Assisted Verification: Scaling To Quadrillions Of Cycles For Verification In The AI Era


The semiconductor industry is at an inflection point. The convergence of advanced multi-die architectures, AI-driven workloads, and rapidly evolving interface protocols is creating unprecedented design complexity. At the same time, market pressures demand faster time-to-market and higher performance, leaving little room for error. From data center to edge developments, users have to run softwar... » read more

Autonomous ASIC Root Cause Analysis


By Mehir Arora and Zackary Glazewski Over 50% of frontend ASIC hardware engineering time is spent on debugging and root cause analysis, spent churning through millions of lines of code and terabytes of waveform data. Despite this, there are no existing solutions for autonomous root cause analysis that use both code and waveform data. ChipAgents Root Cause Analysis (ChipAgents RCA) is the fir... » read more

2025 – A Year Of Change And Anticipation


2025 has certainly been a year of unexpected changes. These had a significant impact on the semiconductor industry and everything that supports it. Not all the changes have been bad, but flexibility has been a requirement for continued success or to make the most of an opportunity provided. Some industries, such as aerospace and defense, are seeing a significant boost around the world. Data ... » read more

Benefits And Limits Of Using ML For Materials Discovery


Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development. Whether the goal is to identify new applications for known materials or to design new molecules for a particular task, these tools help materials scientists find correlations in large data libraries. Still, machine learning tools are not magic. “Software tools are only as... » read more

AI Workloads at the Edge: Ensuring Performance, Privacy, and Security


Experts At The Table: Semiconductor Engineering gathered a group of experts to discuss why some AI workloads are better suited for on-device processing to achieve consistent performance, avoid network connectivity issues, reduce cloud computing costs, and ensure privacy. The panel included Frank Ferro, group director in the Silicon Solutions Group at Cadence; Eduardo Montanez, vice president a... » read more

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