HPC 2017-18 | High-performance computing


trained in the traditional way. ‘We believe AI is in its infancy and more architectures and methods – like Loihi – will continue emerging that raise the bar for AI. ‘Neuromorphic computing draws inspiration


from our current understanding of the brain’s architecture and its associated computations,’ wrote Mayberry. ‘Te brain’s neural networks relay


Nvidia Tesla V100 GPU


algorithms and faster application frameworks is another highly competitive area. At Nvidia’s GTC event in China, held at the


end of September 2017, Nvidia CEO, Jensen Huang, delivered a keynote that focused more on soſtware development and application performance than new GPU hardware. Tere was one hardware tease of a new


processor to aid in developing autonomous vehicles, known as ‘Xavier’, but this will not be unveiled until 2018. Most of the keynote was focused on the


development of new soſtware NVIDIA AI computing platform, TensorRT 3, the AI Cities platform, and Nvidia DRIVE PX. TensorRT 3 is Nvidia’s AI inferencing


soſtware, which has been designed to boost the performance and slash the cost of inferencing from the cloud to edge devices. Tis new soſtware could be used in applications from self- driving cars to robots. During the keynote, Huang claimed that


using TensorRT 3 and Nvidia’s latest GPUs can process up to 5,700 images a second. Nvidia’s DRIVE platform has been designed


as a platform for future autonomous vehicles and Nvidia has stated that users can adopt some or the entire platform. Te systems is based on a scalable architecture that is available in a variety of configurations. Tese range from a single mobile processor, to a multi-chip configuration with two mobile processors and two discrete GPUs, or a combination of multiple DRIVE PX systems used in parallel. Te new processor Xavier will provide an


added boost to this platform in 2018. Early access partners will be able to receive platform containing the Xavier SoC early next year, while general availability is expected at the end of 2018.


12 ‘Our vision is to enable every researcher


everywhere to enable AI for the goodness of mankind,’ said Nvidia CEO Jensen Huang. ‘We believe we now have the fundamental pillars in place to invent the next era of artificial intelligence, the era of autonomous machines.’ Intel has also launched products aimed at


deep learning such as its first neuromorphic, self- learning computer chip codenamed Loihi. Intel released information about the chip as part of a blog post at the end of September authored by Dr Michael Mayberry, corporate vice president and managing director of Intel Labs. Te blog post notes that the work on


neuromorphic computing builds on decades of research that started with CalTech professor,


We believe AI is in its infancy and more architectures and methods – like Loihi – will continue emerging that raise the bar for AI


Carver Mead, who was known for his work in semiconductor design. Te combination of chip expertise, physics and biology yielded some revolutionary ideas: comparing machines with the human brain. As part of this research Intel developed a


self-learning neuromorphic chip – codenamed Loihi – that mimics how the brain functions by learning to operate based on various modes of feedback from the environment. Intel claims that this is an extremely energy-efficient chip, which uses the data to learn and make inferences, gets smarter over time and does not need to be


information with pulses or spikes, modulate the synaptic strengths or weight of the interconnections based on timing of these spikes, and store these changes locally at the interconnections. Intelligent behaviours emerge from the cooperative and competitive interactions between multiple regions within the brain’s neural networks and its environment,’ Mayberry added. Te Blog post from Intel also notes that


ML models such as deep learning have made tremendous recent advancements – particularly in areas such as training neural networks to perform pattern or image recognition. ‘However, unless their training sets have specifically accounted for a particular element, situation or circumstance, these ML systems do not generalise well,’ stated Mayberry. Te Intel blog post noted that the self-


learning capabilities prototyped by this new chip from Intel have the potential to improve automotive and industrial applications as well as personal robotic. Any application that would benefit from autonomous operation and continuous learning – such as recognising the movement of a car or bike – is a potential application for the Loihi chip. In the first half of 2018, the Loihi test chip will


be shared with leading university and research institutions with a focus on advancing AI. In addition to new chips, Intel has also


committed $1 billion to AI research in recent months. Brian Matthew Krzanich, chief executive officer of Intel, stated: ‘We are deeply committed to unlocking the promise of AI: conducting research on neuromorphic computing, exploring new architectures and learning paradigms. We have also invested in startups like Mighty AI, Data Robot and Lumiata through our Intel Capital portfolio and have invested more than $1 billion in companies that are helping to advance artificial intelligence.’ ‘AI solutions require a wide range of power


and performance to meet application needs. To support the sheer breadth of future AI workloads, businesses will need unmatched flexibility and infrastructure optimisation so that both highly specialised and general purpose AI functions can run alongside other critical business workloads,’ Krzanich concluded. l


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