Horizon is often seen as potentially becoming China’s equivalent of NVIDIA. The company is founded by Dr Kai Yu, a prominent industry figure with quite the credentials.

Yu led Baidu’s AI Research lab for three years, founded the Baidu Institute of Deep Learning, and launched the company’s autonomous driving business unit.

Furthermore, Yu has taught at Stanford University, published over 60 papers, and even won first place in the ImageNet challenge which evaluates algorithms for object detection and image classification.

China is yet to produce a chipset firm which can match the capabilities of Western equivalents.

With increasing US sanctions making it more difficult for Chinese firms to access American semiconductors, a number of homegrown companies are emerging and gaining attention from investors.

Horizon is just five-years-old and specialises in making AI chips for robots and autonomous vehicles. The company has already attracted significant funding.

Around two years ago, Horizon completed a $600 million funding round with a $3 billion valuation. The company has secured $150 million so far as part of this latest round.

While it’s likely the incoming Biden administration in the US will take a less strict approach to trade with China, it seems Beijing wants to build more homegrown alternatives which can match or surpass Western counterparts.

Chinese tech giants like Huawei are investing significant resources in their chip manufacturing capabilities to ensure the country has the tech it needs to power groundbreaking advancements like self-driving cars.

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Despite incidents of reckless flying, drones unlock huge opportunities. We regularly see beautiful photography and videography shot using drones—but, of course, they can do so much more.

Sony has built a stellar reputation in media capture. The company builds great cameras – both for itself and sensors it supplies to other manufacturers (like its new IMX686) – and its software like Vegas Pro is the defacto choice for many creative professionals.

In a press release, Sony wrote:

“Airpeak will support the creativity of video creators to the fullest extent possible, aiming to contribute to the further development of the entertainment industry as well as to improve efficiency and savings in various industries.

Airpeak will also promote this project to enable drone-use with the highest level of safety and reliability in the environments where this has been difficult in the past.”

The focus on supporting video creators is to be expected from Sony, but the mention of various industries suggests the company has bigger plans.

In the photography/videography space alone, Sony will face stiff competition from established players like DJI.

Despite being the current industry leader, DJI has begun diversifying its products in recent years due to a decline in drone popularity for consumer purposes. This is mostly due to increasing restrictions in many countries around where drones can fly and even requiring permits (the FAA, for example, requires users to register all drones over a certain size.)

A patent granted to Sony back in January suggests the company may start relatively simple:

However, Sony could use its AI and robotics expertise to stand out in other exciting areas where drones have a lot of potential such as emergency response, delivering supplies, assisting in warehouses/factories, and even tackling small fires before they spread.

The language Sony uses suggests the company will target a wide range of customers from everyday consumers to large enterprise deployments.

Sony plans to reveal further details about Airpeak in the Spring of 2021.

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Amazon Web Services (AWS) first launched a GPU instance 10 years ago with the NVIDIA M2050. It’s rather poetic that, a decade on, NVIDIA is now providing AWS with the hardware to power the next generation of groundbreaking innovations.

The A100 outperformed CPUs in this year’s MLPerf by up to 237x in data centre inference. A single NVIDIA DGX A100 system – with eight A100 GPUs – provides the same performance as nearly 1,000 dual-socket CPU servers on some AI applications.

“We’re at a tipping point as every industry seeks better ways to apply AI to offer new services and grow their business,” said Ian Buck, Vice President of Accelerated Computing at NVIDIA, following the benchmark results.

Businesses can access the A100 in AWS’ P4d instance. NVIDIA claims the instances reduce the time to train machine learning models by up to 3x with FP16 and up to 6x with TF32 compared to the default FP32 precision.

Each P4d instance features eight NVIDIA A100 GPUs. If even more performance is required, customers are able to access over 4,000 GPUs at a time using AWS’s Elastic Fabric Adaptor (EFA).

Dave Brown, Vice President of EC2 at AWS, said:

“The pace at which our customers have used AWS services to build, train, and deploy machine learning applications has been extraordinary. At the same time, we have heard from those customers that they want an even lower-cost way to train their massive machine learning models.

Now, with EC2 UltraClusters of P4d instances powered by NVIDIA’s latest A100 GPUs and petabit-scale networking, we’re making supercomputing-class performance available to virtually everyone, while reducing the time to train machine learning models by 3x, and lowering the cost to train by up to 60% compared to previous generation instances.”

P4d supports 400Gbps networking and makes use of NVIDIA’s technologies including NVLink, NVSwitch, NCCL, and GPUDirect RDMA to further accelerate deep learning training workloads.

Some of AWS’ customers across various industries have already begun exploring how the P4d instance can help their business.

Karley Yoder, VP & GM of Artificial Intelligence at GE Healthcare, commented:

“Our medical imaging devices generate massive amounts of data that need to be processed by our data scientists. With previous GPU clusters, it would take days to train complex AI models, such as Progressive GANs, for simulations and view the results.

Using the new P4d instances reduced processing time from days to hours. We saw two- to three-times greater speed on training models with various image sizes while achieving better performance with increased batch size and higher productivity with a faster model development cycle.”

For an example from a different industry, the research arm of Toyota is exploring how P4d can improve their existing work in developing self-driving vehicles and groundbreaking new robotics.

“The previous generation P3 instances helped us reduce our time to train machine learning models from days to hours,” explained Mike Garrison, Technical Lead of Infrastructure Engineering at Toyota Research Institute.

“We are looking forward to utilizing P4d instances, as the additional GPU memory and more efficient float formats will allow our machine learning team to train with more complex models at an even faster speed.”

P4d instances are currently available in the US East (N. Virginia) and US West (Oregon) regions. AWS says further availability is planned soon.

You can find out more about P4d instances and how to get started here.

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The country’s ICT ministry made the announcement this week as South Korea positions itself to move beyond its historic foothold in memory chips into artificial intelligence semiconductors.

South Korea is investing heavily in AI; especially in the hardware which makes it possible.

Around one trillion won ($871 million) will be spent on developing next-generation AI chips before 2029. The current plan is to be in a position to produce AI chips nationally by 2022 and build a 3,000-strong army of experts within the decade.

Last year, President Moon Jae-in announced a ‘National Strategy for Artificial Intelligence’ (PDF) and set out his desire for South Korea to lead in the technology.

In a foreword, President Moon Jae-in wrote:

“The era of the Fourth Industrial Revolution is indeed an age in which imagination can change the world. Korea is neither the first country to have ushered in the era of artificial intelligence nor the country with the best AI technology at present. However, the country has people capable of turning their imagination into reality and taking on challenges to pursue novelty.

Even in the throes of the 1997 Asian financial crisis, the country led the Internet Revolution and now boasts world-class manufacturing competitiveness, globally unmatched ICT infrastructure and abundant data concerning e-government.

If we link artificial intelligence primarily with the sectors in which we’ve accumulated extensive experience and competitiveness, such as manufacturing and semiconductors, we will be able to give birth to the smartest yet most human-like artificial intelligence. The Government will join forces with developers to help them fully utilize their imaginations and turn their ideas into reality.”

South Korea is home to tech giants such as Samsung and SK hynix which continue to offer global innovations. However, it’s understandable South Korea wants to ensure it secures a slice of what will be a lucrative market.

Analysts from McKinsey predict AI chips will generate around $67 billion in revenue by 2025 and capture around 20 percent of all semiconductor demand.

South Korea, for its part, wants to own 20 percent of the global AI chip market by the end of this decade.

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Bristol-based Graphcore ranked number one on Fast Company’s top 10 most innovative AI companies of 2020 list. Nvidia, for comparison, ranked fifth.

Fast Company’s confidence in Graphcore clearly isn’t misplaced. Announcing its GC200 processor, Graphcore says its new chip is the world’s most complex.

The GC200 processor boasts 59.4 billion transistors and takes the crown from Nvidia’s A100 as the world’s largest. The A100 was announced by Nvidia earlier this year and features 54 billion transistors.

Each GC200 chip has 1,472 independent processor cores and 8,832 separate parallel threads, all supported by 900MB of in-processor RAM.

Graphcore says that up to 64,000 of the 7nm GC200 chips can be linked to create a massive parallel processor with around 16 exaflops of computational power and petabytes of power. Such a system would be able to support AI models with trillions of parameters.

“We are impressed with Graphcore’s technology for energy-efficient construction and execution of large, next-generation ML models, and we expect significant performance gains for several of our AI-oriented research projects in medical imaging and cardiac simulations,” comments Are Magnus Bruaset, Research Director at Simula Research Laboratory.

“We are also pursuing other avenues of research that can push the envelope for Graphcore’s multi-IPU systems, such as how to efficiently conduct large-scale, sparse linear algebra operations commonly found in physics-based HPC workloads.”

The GC200 is just the second chip to be launched by Graphcore. Compared to the first generation, the GC200 delivers an up to 9.3x performance increase.

Graphcore’s founders believe the IPU approach that the company is taking is more efficient than Nvidia’s GPU route. The ability to scale up to thousands of IPU processors in existing compute infrastructures could mean that the cost could be 10-20x lower than using GPUs.

Back in February, Graphcore announced that it had raised $150 million in funding for its R&D. The company’s total valuation is $1.95 billion.

Graphcore was fortunate to have secured its cash before the COVID-19 pandemic really hit – with many startups reporting difficulties obtaining vital funding where there was previous interest. Undoubtedly, the GC200 will help to power research to get us through this pandemic and all the other challenges the world faces now and in the future.

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As the world’s largest smartphone and memory chip manufacturer – along with being a key player in many consumer devices including TVs, washing machines, fridges, and more – Samsung has established itself as a tech leader.

The convergence of powerful emerging technologies such as AI, IoT, and the cloud, have the potential to radically shake-up the industry and dethrone established players if they don’t keep pace. Samsung has made clear it has no intention of giving up its castle.

“In a hyperconnected society via 5G, AI and internet of things, a company that innovates user experiences will be a global business leader,” said Koh Dong-jin, head of IT & mobile communications at Samsung, during an AI forum held at the company’s Seoul R&D Center.

“5G, AI will lay the ground for technology innovations for smartphones, wearables, speakers, internet of things, augmented reality and virtual reality, and provide a turning point for our everyday lives.”

Samsung has seven dedicated AI research centres across five nations; the US, UK, Korea, Canada, and Russia.

The company’s first AI mobile processor with a built-in 5G modem is set for release later this year to simplify the development of highly-capable devices with support for both of the important new technologies.

Amid a global slowdown in smartphone sales, Samsung will be hoping its AI and 5G investments help to fortify its position as a global tech leader in a rapidly-changing world.

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