GPU Accelerated Computing

The computing applications used in semiconductor design and manufacturing have ever-increasing requirements for speed, accuracy and reliability. The continuation of Moore's Law creates a perpetual demand for greater accuracy as, with each new process node, larger numbers of increasingly smaller features are crowded onto each mask and wafer. Computing farms, where thousands of central processing... » read more

Neural Net Computing Explodes

Neural networking with advanced parallel processing is beginning to take root in a number of markets ranging from predicting earthquakes and hurricanes to parsing MRI image datasets in order to identify and classify tumors. As this approach gets implemented in more places, it is being customized and parsed in ways that many experts never envisioned. And it is driving new research into how el... » read more

Heterogeneous System Challenges Grow

As more types of processors are added into SoCs—CPUs, GPUs, DSPs and accelerators, each running a different OS—there is a growing challenge to make sure these compute elements interact properly with their neighbors. Adding to the problem is this mix of processors and accelerators varies widely between different markets and applications. In mobile there are CPUs, GPUs, video and crypto pr... » read more

The Zen Of Processor Design

Mark Papermaster, chief technology officer at Advanced Micro Devices, sat down with Semiconductor Engineering to discuss how to keep improving performance per watt, new packaging options, and the increasing focus on customization for specific tasks. What follows are excerpts of that conversation. SE: As we get more into the IoT and we have to deal with more data, not to mention cars where da... » read more

How Cache Coherency Impacts Power, Performance

Managing how the processors in an SoC talk to one another is no small feat, because these chips often contain multiple processing units and caches. Bringing order to these communications is critical for improving performance and [getkc id="106" kc_name="reducing power"]. But it also requires a detailed understanding of how data moves, the interaction between hardware and software, and what c... » read more

Performance First

Crank up the clock speed. It takes a lot more performance to run virtual reality smoothly, or to process data in the cloud, or to stream a high-definition video from a drone. And none of that compares to the amount of performance required to kill an array of disturbingly realistic zombies on a mobile device in conjunction with other players scattered around the globe. After several years of ... » read more


Nvidia’s new GeForce GTX 1080 gaming graphics card is a piece of work. Employing the company’s Pascal architecture and featuring chips made with a 16nm [getkc id="185" kc_name="finFET"] process, the GTX 1080’s GP104 graphics processing units boast 7.2 billion transistors, running at 1.6 GHz, and it can be overclocked to 1.733 GHz. The die size is 314 mm², 21% smaller than its GeForce ... » read more

The Mightier Microcontroller

Microcontrollers are becoming more complex, more powerful, and significantly more useful, but those improvements come with strings attached. While it's relatively straightforward to develop multi-core microcontroller (MCU) hardware with advanced power management features, it's much more difficult to write software for these chips because memory is limited. CPUs can use on-chip memory such as... » read more

Convolutional Neural Networks Power Ahead

While the term may not be immediately recognizable, convolutional neural networks (CNNs) are already part of our daily lives—and they are expected to become even more significant in the near future. [getkc id="261" kc_name="Convolutional neural networks"] are a form of machine learning modeled on the way the brain's visual cortex distinguishes one object from another. That helps explain wh... » read more

GPU-Based Computing In Photomask Manufacturing

Graphical-processing unit (GPU)-accelerated computing has reached maturity for professional, scientific computing applications. One example of this is the recent GPU-accelerated thermal application for semiconductor photomask manufacturing, which is used in 24/7 manufacturing environments. GPU-accelerated computing won’t be a universal panacea for the semiconductor industry’s “need for sp... » read more

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