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

Where ML Works Best


Anirudh Devgan, president of Cadence, sat down with Semiconductor Engineering to discuss machine learning inside and outside of EDA tools and how that will affect the future of chip and system design. What follows are excerpts of that discussion. SE: How do you see the market and use of machine learning shaping up? Devgan: There are three main areas—machine learning inside, machine lear... » read more

Synthesizing Computer Vision Designs To Hardware


Computer vision is one of the hottest markets in electronic design today. Digital processing of images and video with complex algorithms in order to interpret meaning has almost as many applications and markets as there are uses for the human eye. The biggest problem that designers face is that the computer vision system requirements and algorithms change quickly and often. Even the targ... » read more

Hardware Acceleration With eFPGAs


If integrating an embedded FPGA (eFPGA) into your ASIC or SoC design strikes you as odd, it shouldn’t. ICs have been absorbing almost every component on a circuit board for decades, starting with transistors, resistors, and capacitors –– then progressing to gates, ALUs, microprocessors, and memories. FPGAs are simply one more useful component in the tool box, available for decades and ... » read more

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