Matrix Posted July 16, 2019 Share Posted July 16, 2019 The big picture: One of the recurring themes in computer technology in recent years is the dramatic slowdown of Moore’s Law, and how big companies are scrambling to find creative ways around the limitations of silicon. Now Intel has revealed a neuromorphic system that is more than 1,000 times faster than traditional CPUs at specific tasks. It’s not going to run your games any faster, but it is a boon for self-driving cars and the Internet of Things. Intel had a big show off earlier at DARPA’s Electronics Resurgence Initiative 2019 summit in Detroit, with a project called Pohoiki Beach -- essentially a cluster of 64 of the company’s Loihi chips networked on a Nahuku board, capable of mimicking the self-learning abilities of eight million neurons. The system is able to process information much faster than a traditional CPU+GPU architecture, all while sipping 10,000 times less power than that setup. The tech giant says its neuromorphic chips aren’t a suitable replacement for traditional CPU architecture, instead its potential lies in accelerating specialized applications like constraint-satisfaction problems, graph searches, and sparse coding. Simply put, those algorithms are essential for things like autonomous cars, object-tracking cameras, prosthetics, and artificial skin to name a few. Pohoiki Beach is now headed to over 60 research partners, some of which have already started working on adapting it to real-world problems. The chipmaker claims that even when the system was scaled up 50 times, it was still able to use five times less power. In another application, mapping and location tracking with Loihi chips was just as accurate, but 100 times less power-hungry than a CPU-run solution that’s popular in the industry. Intel promised to scale up Pohoiki Beach to 100 million neurons later this year, paving the way for tiny supercomputers that accelerate AI and other complex tasks, and is also looking at cramming as many Loihi chips as it can into a USB form factor system codenamed Kapoho Bay, ideal for low-power applications. If you’ve been following the discussion around computing technology, you may already know that exotic dreams like MESO quantum computing are still far from becoming a reality. Intel’s been hard at work trying to crack that problem, but its strategy remains centered around chiplets. VIEW: Original Article. Link to comment Share on other sites More sharing options...
Karlston Posted July 16, 2019 Share Posted July 16, 2019 Brains scale better than CPUs. So Intel is building brains The new Pohoiki Beach builds on the 2017 success of Intel's Loihi NPU. Enlarge / This is a picture of an Intel Nahuku board, which can contain 8 to 32 Loihi neuromorphic processing units, interfaced to an Intel Arria 10 FPGA development kit. Intel’s latest neuromorphic system, Pohoiki Beach, is made up of multiple Nahuku boards and contains 64 Loihi chips. Intel Labs Neuromorphic engineering—building machines that mimic the function of organic brains in hardware as well as software—is becoming more and more prominent. The field has progressed rapidly, from conceptual beginnings in the late 1980s to experimental field programmable neural arrays in 2006, early memristor-powered device proposals in 2012, IBM's TrueNorth NPU in 2014, and Intel's Loihi neuromorphic processor in 2017. Yesterday, Intel broke a little more new ground with the debut of a larger-scale neuromorphic system, Pohoiki Beach, which integrates 64 of its Loihi chips. Intel's Jon Tse demonstrates teaching a single Loihi chip to identify new objects in just a few seconds each. Where traditional computing works by running numbers through an optimized pipeline, neuromorphic hardware performs calculations using artificial "neurons" that communicate with each other. This is a workflow that's highly specialized for specific applications, much like the natural neurons it mimics in function—so you likely won't replace conventional computers with Pohoiki Beach systems or its descendants, for the same reasons you wouldn't replace a desktop calculator with a human mathematics major. However, neuromorphic hardware is proving able to handle tasks organic brains excel at much more efficiently than conventional processors or GPUs can. Visual object recognition is perhaps the most widely realized task where neural networks excel, but other examples include playing foosball, adding kinesthetic intelligence to prosthetic limbs, and even understanding skin touch in ways similar to how a human or animal might understand it. Loihi, the underlying chip Pohoiki Beach is integrated from, consists of 130,000 neuron analogs—hardware-wise, this is roughly equivalent to half of the neural capacity of a fruit fly. Pohoiki Beach scales that up to 8 million neurons—about the neural capacity of a zebrafish. But what's perhaps more interesting than the raw computational power of the new neural network is how well it scales. With the Loihi chip we’ve been able to demonstrate 109 times lower power consumption running a real-time deep learning benchmark compared to a GPU, and 5 times lower power consumption compared to specialized IoT inference hardware. Even better, as we scale the network up by 50 times, Loihi maintains real-time performance results and uses only 30 percent more power, whereas the IoT hardware uses 500 percent more power and is no longer real-time. Chris Eliasmith, co-CEO of Applied Brain Research and professor at University of Waterloo Pohoiki Beach appears to be step two of Intel's process-architecture-optimization development model. Step three, a larger integration of Loihi chips to be called Pohoiki Springs, is scheduled to debut later this year. Neuromorphic design is still in a research phase, but this and similar projects from competitors such as IBM and Samsung should break ground for eventual commoditization and commercial use. Source: Brains scale better than CPUs. So Intel is building brains (Ars Technica) (To view the article's image gallery, please visit the above link) Link to comment Share on other sites More sharing options...
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