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Ten Lessons From Three Generations Shaped Google’s TPUv4i


Source: Norman P. Jouppi, Doe Hyun Yoon, Matthew Ashcraft, Mark Gottscho, Thomas B. Jablin, George Kurian, James Laudon, Sheng Li, Peter Ma, Xiaoyu Ma, Nishant Patil, Sushma Prasad, Clifford Young, Zongwei Zhou (Google); David Patterson (Google / Berkeley) Find paper here. 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA) Abstract–"Google deployed s... » read more

Developers Turn To Analog For Neural Nets


Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that’s starting to change. “Everyon... » read more

Improving the Performance Of Deep Neural Networks


Source: North Carolina State University. Authors: Xilai Li, Wei Sun, and Tianfu Wu Abstract: "In state-of-the-art deep neural networks, both feature normalization and feature attention have become ubiquitous. They are usually studied as separate modules, however. In this paper, we propose a light-weight integration between the two schema and present Attentive Normalization (AN). Instead of l... » read more

Power/Performance Bits: Nov. 17


NVMe controller for research Researchers at the Korea Advanced Institute of Science and Technology (KAIST) developed a non-volatile memory express (NVMe) controller for storage devices and made it freely available to universities and research institutions in a bid to reduce research costs. Poor accessibility of NVMe controller IP is hampering academic and industrial research, the team argue... » read more

System Bits: June 18


Another win for aUToronto Photo credit: University of Toronto The University of Toronto’s student-led self-driving car team racked up its second consecutive victory last month at the annual AutoDrive Challenge in Ann Arbor, Mich. The three-year challenge goes out to North American universities, offering a Chevrolet Bolt electric vehicle to outfit with autonomous driving technology.... » read more

Bridging Machine Learning’s Divide


There is a growing divide between those researching [getkc id="305" comment="machine learning"] (ML) in the cloud and those trying to perform inferencing using limited resources and power budgets. Researchers are using the most cost-effective hardware available to them, which happens to be GPUs filled with floating point arithmetic units. But this is an untenable solution for embedded infere... » read more

System Bits: Nov. 7


Exposing logic errors in deep neural networks In a new approach meant to brings transparency to self-driving cars and other self-taught systems, researchers at Columbia and Lehigh universities have come up with a way to automatically error-check the thousands to millions of neurons in a deep learning neural network. Their tool — DeepXplore — feeds confusing, real-world inputs into the ... » read more

System Bits: Sept. 12


Neural network cautionary tale As machine learning and neural networks proliferate widely today, there is a need to exercise caution in how they are employed, according to Stanford University researchers Michal Kosinki and Yilun Wang. In a study conducted recently, they have shown that deep neural networks can be used to determine the sexual orientation of a person, and caution that this ma... » read more

What’s New At Hot Chips


By Jeff Dorsch & Ed Sperling Machine learning, artificial intelligence and neuromorphic computing took center stage at Hot Chips 2017 this week, a significant change from years past where the focus was on architectures that addressed improvements in speed and performance for standard compute problems. What is clear, given the focus of presentations, is that the bleeding edge of comput... » read more

The Great Machine Learning Race


Processor makers, tools vendors, and packaging houses are racing to position themselves for a role in machine learning, despite the fact that no one is quite sure which architecture is best for this technology or what ultimately will be successful. Rather than dampen investments, the uncertainty is fueling a frenzy. Money is pouring in from all sides. According to a new Moor Insights report,... » read more

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