Integrity Problems For Edge Devices


Battery-powered edge devices need to save every picojoule of energy they can, which often means running at very low voltages. This can create signal and power integrity issues normally seen at the very latest technology nodes. But because these tend to be lower-volume, lower-cost devices, developers often cannot afford to perform the same level of analysis on these devices. Noise can come in... » read more

EDA On Board With New Package Options


A groundswell of activity around multi-die integration and advanced packaging is pushing EDA companies to develop integration strategies that speed up time to sign-off, increase confidence that a design will work as expected, while still leaving enough room for highly customized solutions. Challenges range from how to architect a design, how to explore the best options and configurations, ho... » read more

Power Impact At The Physical Layer Causes Downstream Effects


Data movement is rapidly emerging as one of the top design challenges, and it is being complicated by new chip architectures and physical effects caused by increasing density at advanced nodes and in multi-chip systems. Until the introduction of the latest revs of high-bandwidth memory, as well as GDDR6, memory was considered the next big bottleneck. But other compute bottlenecks have been e... » read more

Signal Integrity Through The Years


Yesterday, I started to talk about how new technologies find their way over time into EDA tools in my post How Technologies Get into EDA. Let's look at signal integrity as an example. We used not to worry about signal integrity at all. The first time anything like that impinged on my consciousness was in the early 1980s when we realized that we needed to start to consider the inductance... » read more

Thermal Challenges In Advanced Packaging


CT Kao, product management director at Cadence, talks with Semiconductor Engineering about why packaging is so complicated, why power and heat vary with different use cases and over time, and why a realistic power map is essential particularly for AI chips, where some circuits are always on.   Interested in more Semiconductor Engineering videos? Sign-up for our YouTube channel here » read more

GDDR6 Drilldown: Applications, Tradeoffs And Specs


Frank Ferro, senior director of product marketing for IP cores at Rambus, drills down on tradeoffs in choosing different DRAM versions, where GDDR6 fits into designs versus other types of DRAM, and how different memories are used in different vertical markets. » read more

Pushing Memory Harder


In an optimized system, no component is waiting for another component while there is useful work to be done. Unfortunately, this is not the case with the processor/memory interface. Put simply, memory cannot keep up. Accessing memory is slow, and it can consume a significant fraction of the power budget. And the general consensus is this problem is not going away anytime soon, despite effort... » read more

Multiphysics Simulations for AI Silicon to System Success


Achieving power efficiency, power integrity, signal integrity, thermal integrity and reliability is paramount for enabling product success by overcoming the challenges of size and complexity in AI hardware and optimizing the same for rapidly evolving AI software. ANSYS’ comprehensive chip, package and system solutions empower AI hardware designers by breaking down design margins and siloed de... » read more

GDDR Accelerates Artificial Intelligence And Machine Learning


The origins of modern graphics double data rate (GDDR) memory can be traced back to GDDR3 SDRAM. Designed by ATI Technologies, GDDR3 made its first appearance in NVidia’s GeForce FX 5700 Ultra card which debuted in 2004. Offering reduced latency and high bandwidth for GPUs, GDDR3 was followed by GDDR4, GDDR5, GDDR5X and the latest generation of GDDR memory, GDDR6. GDDR6 SGRAM supports a ma... » read more

Blog Review: June 19


Mentor's Rebecca Lord digs into signal integrity complications and why today's high frequency signals make it important to understand the physics of transmission lines. Cadence's Meera Collier points to the need to recognize diversity and nuance when compiling AI training datasets and avoid the oversimplification that can lead to bias. Synopsys' Deepak Nagaria checks out the new features ... » read more

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