Power/Performance Bits: Jan. 22


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


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


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


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?


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


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

High Neural Inferencing Throughput At Batch=1


Microsoft presented the following slide as part of their Brainwave presentation at Hot Chips this summer: In existing inferencing solutions, high throughput (and high % utilization of the hardware) is possible for large batch sizes: this means that instead of processing say one image at a time, the inferencing engine processes say 10 or 50 images in parallel. This minimizes the number of... » read more

Inferencing In Hardware


Cheng Wang, senior vice president of engineering at Flex Logix, examines shifting neural network models, how many multiply-accumulates are needed for different applications, and why programmable neural inferencing will be required for years to come. https://youtu.be/jb7qYU2nhoo         See other tech talk videos here. » read more

Looking Beyond The CPU


CPUs no longer deliver the same kind of of performance improvements as in the past, raising questions across the industry about what comes next. The growth in processing power delivered by a single CPU core began stalling out at the beginning of the decade, when power-related issues such as heat and noise forced processor companies to add more cores rather than pushing up the clock frequency... » 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

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