Optimizing Power And Performance For Machine Learning At The Edge


While machine learning (ML) algorithms are popular for running on enterprise Cloud systems for training neural networks, AI/ML chipsets for edge devices are growing at a triple digit rate, according to Tractica “Deep Learning Chipsets” (Figure 1). Edge devices include automobiles, drones, and mobile devices that are all employing AI/ML to provide valuable functionality. Figure 1: Marke... » read more

How to Manage One Trillion Devices on the Edge


THE EDGE, THE DATACENTER, AND NEW DESIGN PRINCIPLES: The world of compute is changing rapidly, as is the traditional view of a physical building, or buildings filled with servers, storage, and networking to “run the business”. Cloud computing, distributed cloud computing, and edge computing will all be fed by a 5G access network, forcing IT organizations to think and plan differently. Th... » read more

CXL Vs. CCIX


Kurt Shuler, vice president of marketing at ArterisIP, explains how these two standards differ, which one works best where, and what each was designed for. » read more

Where Is The Edge AI Market And Ecosystem Headed?


Until recently, most AI was in datacenters and most was training. Things are changing quickly. Projections are AI sales will grow rapidly to $10s of billions by the mid 2020s, with most of the growth in Edge AI Inference. Edge inference applications Where is the Edge Inference market today? Let’s look at the markets from highest throughput to lowest. Edge Servers Recently Nvidia annou... » read more

Reducing Data At The Source


Jens Döge, group manager for image acquisition and processing in Fraunhofer IIS’ Engineering of Adaptive Systems Division, talks about how to slash the amount of data that needs to be sent to the cloud or edge for processing by focusing only on the regions of interest in an image, and how that reduces the cost of moving that data. » read more

Leveraging Data In Chipmaking


John Kibarian, president and CEO of PDF Solutions, sat down with Semiconductor Engineering to talk about the impact of data analytics on everything from yield and reliability to the inner structure of organizations, how the cloud and edge will work together, and where the big threats are in the future. SE: When did you recognize that data would be so critical to hardware design and manufact... » read more

Less Food, More Thought


A trillion "things" are expected to be connected to the Internet sometime in the next decade. No matter how power-efficient these things are, they probably will require enough coin-sized lithium batteries to drain the world's supply of element No. 3 on the Periodic Table. They also will increase the demand for power everywhere, and that's even before tacking on electric vehicles, the edge, robo... » read more

More Data, More Processing, More Chips


Simon Segars, CEO of Arm, sat down with Semiconductor Engineering to talk about the impact of heterogeneous computing and new packaging approaches on IP, the need for more security, and how 5G and the edge will impact compute architectures and the chip industry. SE: There are a whole bunch of new markets opening up. How does Arm plan to tackle those? Segars: Luckily for us, we can design ... » read more

Memory Subsystems In Edge Inferencing Chips


Geoff Tate, CEO of Flex Logix, talks about key issues in a memory subsystem in an inferencing chip, how factors like heat can affect performance, and where these kinds of chips will be used. » read more

VC Perspectives On An AI Summer


It’s been a busy summer for Applied Ventures. Our team has had many interactions in the startup and investing space, and added some new companies to our portfolio. I’ll be sharing highlights of these activities in a series of upcoming blogs, but first I’d like to reflect on current market developments in machine learning and how they are affecting VC investment patterns. Strategic inve... » read more

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