Preparing For War On The Edge


War clouds are gathering over the edge of the network. The rush by the reigning giants of data—IBM, Amazon, Facebook, Alibaba, Baidu, Microsoft and Apple—to control the cloud by building mammoth hyperscale data centers  is being met with uncertainty at the edge of the network. In fact, just the emergence of the edge could mean that all bets are off when it comes to data dominance. It... » read more

AI: Where’s The Money?


A one-time technology outcast, Artificial Intelligence (AI) has come a long way. Now there’s groundswell of interest and investments in products and technologies to deliver high performance visual recognition, matching or besting human skills. Equally, speech and audio recognition are becoming more common and we’re even starting to see more specialized applications such as finding optimized... » read more

Pushing AI Into The Mainstream


Artificial intelligence is emerging as the driving force behind many advancements in technology, even though the industry has merely scratched the surface of what may be possible. But how deeply AI penetrates different market segments and technologies, and how quickly it pushes into the mainstream, depend on a variety of issues that still must be resolved. In addition to a plethora of techni... » read more

Cutting The Cord: How Edge Intelligence Is Enabling The IoT To Go Where Cloud Can’t


In a world where data’s time to value or irrelevancy may be measured in milliseconds, the latency introduced in transferring data to the cloud threatens to undermine many of the Internet of Things’ most compelling use cases. Think of data as the fuel that powers our new decision-making engines – fail to get the fuel to the engines fast enough and the engine splutters and dies. Meanwhil... » read more

AI Chip Architectures Race To The Edge


As machine-learning apps start showing up in endpoint devices and along the network edge of the IoT, the accelerators that make AI possible may look more like FPGA and SoC modules than current data-center-bound chips from Intel or Nvidia. Artificial intelligence and machine learning need powerful chips for computing answers (inference) from large data sets (training). Most AI chips—both tr... » read more

IIoT Edge Is A Moving Target


Edge computing happens in an industrial IoT (IIoT) system wherever it needs to happen. The business needs for an IIoT system—or one layer of that system—will determine when and where the computing happens. This conclusion, from an introductory report written by the IoT testing organization the Industrial Internet Consortium (IIC), helps explain why no one consistently can say what edge... » read more

Optimizing 5G With AI At The Edge


AI touches our lives in many different ways, and while some AI-enabled applications are highly visible, like the increasingly popular Amazon Echo and Google Home voice-controlled intelligent digital assistants, others are less obvious. But by no means are they less important. For example, AI techniques are essential to the successful rollout of 5G wireless communications. 5G is the develop... » read more

More Processing Everywhere


Simon Segars, CEO of Arm Holdings, sat down with Semiconductor Engineering to discuss security, power, the IoT, a big push at the edge, and the rise of 5G and China. What follows are excerpts of that conversation. SE: Are we making any progress in security? And even if Arm makes progress, does it matter, given there are so many things connected together? Segars: It feels like we’re maki... » read more

Pace Quickens As Machine Learning Moves To The Edge


Artificial intelligence applications are rapidly changing the way society engages with technology. It wasn’t too long ago that your smart phone couldn’t recognize your face or your thumbprint. It also wasn’t too long ago that Alexa wasn’t helping you navigate your day so easily. And not too long ago, odds are, you weren’t developing an application or device that had AI/ML as its ce... » read more

Where The Rubber Hits The Road: Implementing Machine Learning On Silicon


Machine learning (ML) is everywhere these days. The common thread between advanced driver-assistance systems (ADAS) vision applications in our cars and the voice (and now facial) recognition applications in our phones is that ML algorithms are doing the heavy lifting, or more accurately, the inferencing. In fact, neural networks (NN) can even be used in application spaces such as file compressi... » read more

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