Memory In Microcontrollers


Gideon Intrater, CTO of Adesto, talks about how to use microcontrollers for applications where more memory is required, such as automotive, communication, and AI at the edge. Options include moving MCUs toward a more aggressive process node, adding external non-volatile memory, and execute-in-place types of architectures. » read more

Where 5G Works, And Where It Doesn’t


The rollout of 5G hype has begun. Companies are building 5G chipsets for mobile devices, and they are working on the infrastructure that will allow massive amounts of data to move freely between devices. There is little doubt that more bandwidth is required everywhere. Files are growing in size, particularly with streaming video and images and various flavors of AI and machine learning. This... » read more

Empowering UPF Commands With Effective Elements Lists


The Unified Power Format (UPF) is intended for power management, power aware verification, and low power implementation. The more we explore the inherent features of UPF commands and options, and comprehend their interrelation, the more we become accurate, productive, and consistent in developing UPF for our intended purposes. Although the UPF is very well defined through the IEEE 1801 LRM, ... » 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

Accurate Power Analysis Using Real Software Workloads


Over the last decade or so, power consumption has become a major issue in the design of many types of electronic products. Of course, power has always mattered for battery-operated devices, but the complexity of portable electronics and the size of the chips they contain have grown significantly. For plugged-in devices, from desktop computers to server racks in a data center, power plays a majo... » read more

GDDR Accelerates Artificial Intelligence And Machine Learning


The origins of modern graphics double data rate (GDDR) memory can be traced back to GDDR3 SDRAM. Designed by ATI Technologies, GDDR3 made its first appearance in NVidia’s GeForce FX 5700 Ultra card which debuted in 2004. Offering reduced latency and high bandwidth for GPUs, GDDR3 was followed by GDDR4, GDDR5, GDDR5X and the latest generation of GDDR memory, GDDR6. GDDR6 SGRAM supports a ma... » read more

Low-Power Design Becomes Even More Complex


Throughout the SoC design flow, there has been a tremendous amount of research done to ease the pain of managing a long list of power-related issues. And while headway has been made, the addition of new application areas such as AI/ML/DL, automotive and IoT has raised as many new problems as have been solved. The challenges are particularly acute at leading-edge nodes where devices are power... » read more

Using Technology To Improve Beer And Wine


For those people who enjoy a glass of Cabernet, Sauvignon Blanc, or Pilsner, you know that the temperature of that beverage is key to its enjoyment. Serve a red wine too chilled, and it ruins the flavor. And let’s not even get into the flavor of a warm lager. If you’re curious about how temperature affects the flavor of wine, check out this short and entertaining video from Wine Folly. Ask... » read more

Will In-Memory Processing Work?


The cost associated with moving data in and out of memory is becoming prohibitive, both in terms of performance and power, and it is being made worse by the data locality in algorithms, which limits the effectiveness of cache. The result is the first serious assault on the von Neumann architecture, which for a computer was simple, scalable and modular. It separated the notion of a computatio... » read more

What’s Powering Artificial Intelligence


To scale artificial intelligence (AI) and machine learning (ML), hardware and software developers must enable AI/ML performance across a vast array of devices. This requires balancing the need for functionality alongside security, affordability, complexity and general compute needs. Fortunately, there’s a solution hiding in plain sight. To read more, click here (scroll down to "Download No... » read more

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