AI Accelerator Gyrfalcon Soars Post Stealth


Milpitas, Calif.-based startup Gyrfalcon Technology Inc. (GTI), which emerged from semi-stealth mode in September, recently announced the datacenter-focused second generation of its neural-network accelerator, which was first aimed at the endpoint. GTI is not alone: The endpoint market is growing. By 2022, 25% of endpoint devices will execute AI algorithms (inference for neural network appli... » read more

Building AI SoCs


Ron Lowman, strategic marketing manager at Synopsys, looks at where AI is being used and how to develop chips when the algorithms are in a state of almost constant change. That includes what moves to the edge versus the data center, how algorithms are being compressed, and what techniques are being used to speed up these chips and reduce power. https://youtu.be/d32jtdFwpcE    ... » read more

The Next Big Chip Companies


Rambus’ Mike Noonen looks at why putting everything on a single die no longer works, what comes after Moore’s Law, and what the new business model looks like for chipmakers. https://youtu.be/X6Kca8Vm-wA » read more

Intel’s Next Move


Gadi Singer, vice president and general manager of Intel's Artificial Intelligence Products Group, sat down with Semiconductor Engineering to talk about Intel's vision for deep learning and why the company is looking well beyond the x86 architecture and one-chip solutions. SE: What's changing on the processor side? Singer: The biggest change is the addition of deep learning and neural ne... » read more

Power/Performance Bits: July 31


Training optical neural networks Researchers from Stanford University used an optical chip to train an artificial neural network, a step that could lead to faster, more efficient AI tasks. Although optical neural networks have been recently demonstrated, the training step was performed using a model on a traditional digital computer and the final settings were then imported into the optical... » read more

Tech Talk: ISO 26262 Drilldown


ArterisIP’s Kurt Shuler looks at what can go wrong in automotive design, what are the prerequisites for getting the attention of Tier 1 and OEMs, and what’s involved in automotive design at all levels. https://youtu.be/nnjAldn-nKU » read more

IBM Takes AI In Different Directions


Jeff Welser, vice president and lab director at IBM Research Almaden, sat down with Semiconductor Engineering to discuss what's changing in artificial intelligence and what challenges still remain. What follows are excerpts of that conversation. SE: What's changing in AI and why? Welser: The most interesting thing in AI right now is that we've moved from narrow AI, where we've proven you... » read more

When AI Goes Awry


The race is on to develop intelligent systems that can drive cars, diagnose and treat complex medical conditions, and even train other machines. The problem is that no one is quite sure how to diagnose latent or less-obvious flaws in these systems—or better yet, to prevent them from occurring in the first place. While machines can do some things very well, it's still up to humans to devise... » read more

What’s Next In Neuromorphic Computing


To integrate devices into functioning systems, it's necessary to consider what those systems are actually supposed to do. Regardless of the application, [getkc id="305" kc_name="machine learning"] tasks involve a training phase and an inference phase. In the training phase, the system is presented with a large dataset and learns how to "correctly" analyze it. In supervised learning, the data... » read more

Customizing Power And Performance


Designing chips is getting more difficult, and not just for the obvious technical reasons. The bigger issue revolves around what these chips going to be used for-and how will they be used, both by the end user and in the context of other electronics. This was a pretty simple decision when hardware was developed somewhat independently of software, such as in the PC era. Technology generally d... » read more

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