Why More CPUs Are Needed For Agentic AI


The shift from generative AI to agentic AI will significantly increase the amount of compute power needed in data centers. Queries to search for and analyze data from multiple sources will be performed simultaneously by agents and without human intervention, rather than a single request from a live person. Jeff Defilippi, senior director of product management at Arm, talks about the impact of r... » read more

State Of The Market For Edge Silicon


The explosion of data and the rapid ramp of AI is causing significant changes in how chips are architected. At the edge, the key metrics are power, latency, and performance, but those can vary significantly by application and by workload. Steve Roddy, chief marketing officer at Quadric, talks about the need to balance performance and efficiency with flexibility for different applications, what ... » read more

Memory For AI At The Edge


Inferencing at the edge has very different needs than training large language models or large-scale inferencing in AI data centers. Many edge devices run on a battery. They're price-sensitive, and they are constrained by the physical area of the device. As a result, the amount of memory that can be packed into these devices is also limited. Steve Woo, Rambus fellow and distinguished inventor, t... » read more

How AI Will Automate Chip Design


AI has been used in EDA for many years for the core algorithms in tools, but it's getting smarter and more optimized with the rollout of generative and agentic AI. As it evolves and improves, hardware engineers are finding ways to leverage it for more complex tasks. Ziyad Hanna, corporate vice president at Cadence, talks about five levels of autonomy in chip design that mirror those in the auto... » read more

Improving Yield Through Shared Data


Increasing complexity due to advanced packaging, multi-die assemblies, and more devices under test is having an impact on yield, which in turn slows time to market and impacts overall chip costs. What's needed is a way to share data that previously was siloed by chipmakers, fabs, and OSATs. Jayant D'Souza, technical product director at Siemens EDA, talks about the underlying drivers for sharing... » read more

New Challenges In Signoff


Multi-die assemblies coupled with leading-edge process nodes make signoff increasingly challenging and scary. There are more corner cases and more data to consider, but no slack in the delivery schedule. Marc Heyberger, product engineer group director at Cadence Design Systems, talks about full-chip timing, flat versus hierarchical timing analysis, the ongoing development of full 3D-ICs, and wh... » read more

New Performance Requirements For Audio


Demand for higher performance in audio is rising as human-machine interactions increase on the edge. That means more processing elements, and more challenges in keeping data consistent across those processors. Prakash Madhvapathy, director of product marketing and product management at Cadence, talks about the advantages of coherent designs, how that impacts security, and how DSPs are evolving ... » read more

Wi-Fi 7 Moves To The IoT


Wi-Fi 7 has been a staple in high-end applications such as notebook computers and AR/VR glasses for the past couple years, where high-speed connectivity and low latency are essential. Known alternatively as IEEE 802.11be, and Extremely High Throughput Wi-Fi, it is starting to migrate downstream into IoT devices such as smart door locks, thermostats, and robotic vacuum cleaners. But the reason i... » read more

Changes In Chip Architectures At The Edge


Edge computing is all about low latency, within a tight power budget, and with sufficient performance. This is very different from an AI data center, where the real focus is on data throughput between processor and memory. Achieving those goals requires a focus on what different processing elements bring to the table. Nigel Drego, co-founder and CTO of Quadric, talks about how these different c... » read more

Agentic AI In Chip Manufacturing


Agentic AI — breaking AI into individual agents that can work together and collaboratively — will be the real game changer for AI in chip manufacturing. By taking humans out of the loop, these agents can be programmed using natural language to automatically solve problems and improve efficiency. Jon Herlocker, vice president and general manager of software analytics at Cohu, talks  about w... » read more

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