tag: neural networks

Use Inference Benchmarks Similar To Your Application
By Geoff Tate - 07 Feb, 2019 - Comments: 0

If an Inference IP supplier or Inference Accelerator Chip supplier offers a benchmark, it is probably ResNet-50. As a result, it might seem logical to use ResNet-50 to compare inference offerings. If you plan to use ResNet-50 it would be; but if your target application model is significantly different from Resnet-50 it could lead you to pick an inference offering that is not best for you. ... » read more

In-Memory Computing Challenges Come Into Focus
By Katherine Derbyshire - 06 Feb, 2019 - Comments: 6

For the last several decades, gains in computing performance have come by processing larger volumes of data more quickly and with superior precision. Memory and storage space are measured in gigabytes and terabytes now, not kilobytes and megabytes. Processors operate on 64-bit rather than 8-bit chunks of data. And yet the semiconductor industry’s ability to create and collect high quality ... » read more

Power/Performance Bits: Jan. 29
By Jesse Allen - 29 Jan, 2019 - Comments: 0

Neural nets struggle with shape Cognitive psychologists at the University of California Los Angeles investigated how deep convolutional neural networks identify objects and found a big difference between the way these networks and humans perceive objects. In the first of a series of experiments, the researchers showed color images of animals and objects that had been altered to have a diffe... » read more

What’s Next For AI, Quantum Chips
By Mark LaPedus - 24 Jan, 2019 - Comments: 1

Semiconductor Engineering sat down to discuss the latest R&D trends with Luc Van den hove, president and chief executive of Imec; Emmanuel Sabonnadière, chief executive of Leti; and An Chen, executive director for the Nanoelectronics Research Initiative at the Semiconductor Research Corp. (SRC). Chen is on assignment from IBM. What follows are excerpts of those conversations, which took pl... » read more

Power/Performance Bits: Jan. 22
By Jesse Allen - 22 Jan, 2019 - Comments: 0

Efficient neural net training Researchers from the University of California San Diego and Adesto Technologies teamed up to improve neural network training efficiency with new hardware and algorithms that allow computation to be performed in memory. The team used an energy-efficient spiking neural network for implementing unsupervised learning in hardware. Spiking neural networks more closel... » read more

Hardware Mathematics for Artificial Intelligence
By Synopsys - 16 Jan, 2019 - Comments: 0

Article written by John A. Swanson, Sr. Product Marketing Manager, Synopsys Artificial intelligence (AI) has the potential to fundamentally change the way we interact with our devices and live our lives. Petabytes of data efficiently travels between edge devices and data centers for processing and computing of AI tasks. The ability to process real world data and create mathematical represen... » read more

AI In Chip Manufacturing
By Ed Sperling - 10 Jan, 2019 - Comments: 0

Ira Leventhal, New Concept Product Initiative vice president at Advantest, talks with Semiconductor Engineering about using analysis and deep learning to make test more efficient and more effective. https://youtu.be/3VVG4JVnjHo » read more

Edge Inferencing Challenges
By Ed Sperling - 10 Jan, 2019 - Comments: 0

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

What’s the Right Path For Scaling?
By Mark LaPedus - 02 Jan, 2019 - Comments: 1

The growing challenges of traditional chip scaling at advanced nodes are prompting the industry to take a harder look at different options for future devices. Scaling is still on the list, with the industry laying plans for 5nm and beyond. But less conventional approaches are becoming more viable and gaining traction, as well, including advanced packaging and in-memory computing. Some option... » read more

Enabling Embedded Vision Neural Network DSPs
By Cadence - 19 Dec, 2018 - Comments: 0

Neural networks are now being developed in a variety of technology segments in the embedded market, from mobile to surveillance to the automotive segment. The computational and power requirements to process this data is increasing, with new methods to approach deep learning challenges emerging every day. Vision processing systems must be designed holistically, for all platforms, with hardwa... » read more

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