IP Management And Development At 5/3nm

By Ann Mutschler - 24 Oct, 2019 - Comments: 1

The growing complexity of moving to new process nodes is making it much more difficult to create, manage and re-use IP. There are more rules, more data to manage, and more potential interactions as density increases, both in planar implementations and in advanced packaging. And the problems only get worse as designs move to 5nm and 3nm, and as more heterogeneous components such as accelerato... » read more

Solving The Memory Bottleneck

By Brian Bailey - 04 Oct, 2019 - Comments: 4

Chipmakers are scrambling to solve the bottleneck between processor and memory, and they are turning out new designs based on different architectures at a rate no one would have anticipated even several months ago. At issue is how to boost performance in systems, particularly those at the edge, where huge amounts of data need to be processed locally or regionally. The traditional approach ha... » read more

New Vision Technologies For Real-World Applications

By Gordon Cooper - 03 Oct, 2019 - Comments: 0

Computer vision – the ability of a machine to ‘infer’ or extract useful information from a two-dimensional image or an uncompressed video stream of images – has the ability to change our lives. It can enable self-driving cars, empower robots or drones to see their way to delivering packages to your doorstep, and can turn your face into a payment method (Figure 1). To achieve these advan... » read more

Power Is Limiting Machine Learning Deployments

By Brian Bailey - 25 Jul, 2019 - Comments: 3

The total amount of power consumed for machine learning tasks is staggering. Until a few years ago we did not have computers powerful enough to run many of the algorithms, but the repurposing of the GPU gave the industry the horsepower that it needed. The problem is that the GPU is not well suited to the task, and most of the power consumed is waste. While machine learning has provided many ... » read more

Edge Inferencing Challenges

By Ed Sperling - 10 Jan, 2019 - Comments: 0

Geoff Tate, CEO of Flex Logix, talks about balancing different variables to improve performance and reduce power at the lowest cost possible in order to do inferencing in edge devices. https://youtu.be/1BTxwew--5U » read more

AI Market Ramps Everywhere

By Brian Bailey - 20 Dec, 2018 - Comments: 0

Artificial Intelligence (AI) has inspired the general populace, but its rapid rise over the past few years has given many people pause. From realistic concerns about robots taking over jobs to sci-fi scares about robots more intelligent than humans building ever smarter robots themselves, AI inspires plenty of angst. Within the technology industry, we have a better understanding about the pote... » read more

Making Sure A Heterogeneous Design Will Work

By Ed Sperling - 03 Dec, 2018 - Comments: 0

An explosion of various types of processors and localized memories on a chip or in a package is making it much more difficult to verify and test these devices, and to sign off with confidence. In addition to timing and clock domain crossing issues, which are becoming much more difficult to deal with in complex chips, some of the new devices are including AI, machine learning or deep learning... » read more

Making AI Run Faster

By Ed Sperling - 11 Oct, 2018 - Comments: 0

The semiconductor industry has woken up to the fact that heterogeneous computing is the way forward and that inferencing will require more than a GPU or a CPU. The numbers being bandied about by the 30 or so companies working on this problem are 100X improvements in performance. But how to get there isn't so simple. It requires four major changes, as well as some other architectural shifts. ... » read more

The Revenge Of The Digital Twins

By Frank Schirrmeister - 27 Sep, 2018 - Comments: 0

How do we verify artificial intelligence? Even before “smart digital twins” get as advanced as shown in science fiction shows, making sure they are “on our side” and don’t “go rogue” will become a true verification problem. There are some immediate tasks the industry is working on—like functional safety and security—but new verification challenges loom on the horizon. As in pr... » read more

AI, ML Chip Choices

By Ed Sperling - 07 Aug, 2018 - Comments: 0

Flex Logix’s Cheng Wang talks about which types of chips work best for neural networks, AI and machine learning. https://youtu.be/k7OdP7B10o8 » read more

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Knowledge Centers
Entities, people and technologies explored

Shift Left Is The Tip Of The Iceberg

A transformative change is underway for semiconductor design and EDA. New languages, models, and abstractions will need to be created.

by Brian Bailey

Partitioning In The Chiplet Era

Understanding how chiplets interact under different workloads is critical to ensuring signal integrity and optimal performance in heterogeneous designs.

by Ann Mutschler

NAND Flash Targets 1,000 Layers

New techniques go beyond improved deposition and etching, but challenges stack up, too.

by Bryon Moyer

3.5D: The Great Compromise

Pros and cons of a middle-ground chiplet assembly that combines 2.5D and 3D-IC.

by Ed Sperling

AI’s Role In Chip Design Widens, Drawing In New Startups

Focus is on letting engineers do much more with the same or fewer resources — and less drudgery.

by Karen Heyman

What Comes After HBM For Chiplets

The standard for high-bandwidth memory limits design freedom at many levels, but that is required for interoperability. What freedoms can be taken from other functions to make chiplets possible?

by Brian Bailey

Memory Fundamentals For Engineers

eBook: Nearly everything you need to know about memory, including detailed explanations of the different types of memory; how and where these are used today; what's changing, which memories are successful and which ones might be in the future; and the limitations of each memory type.

by The SE Staff

Why Small Fab And Assembly Houses Are Thriving

Megafabs churning out the most advanced processors are not the only game in town.