IP Management And Development At 5/3nm


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


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


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


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


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


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


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


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


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


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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