In September 2021, AI inside started the provision of "AI inside Cube Pro," the highest performance in the series of our in-house designed edge computing device "AI inside Cube." For AI inside to realize a society in which AI has spread to every corner of society, "AI inside Cube" is an essential element that composes the platform for anyone to create and use AI easily. As a company that provides AI services, we will introduce why we design and provide in-house developed edge computing devices in addition to software, and the future that we are aiming for. There are two primary environments to run AI; cloud computing service and edge computing service. Cloud computing service is when AI training and inference is processed in the cloud, and "cloud AI" refers to running AI in the cloud.

First published on March 12, 2021, on Hewlett Packard Enterprise's Enterprise.nxt, publishing insights about the future of technology. Edge computing and AI promise to make healthcare delivery cheaper, easier, and better for everyone. It couldn't happen at a better time. Here's one problem the pandemic has underscored: A healthcare system that was not terribly efficient to begin with now seems stressed to the breaking point. Infectious diseases are on the rise.

Socionext Inc. has developed a prototype chip that incorporates newly-developed quantized Deep Neural Network (DNN) technology, enabling highly-advanced AI processing for small and low-power edge computing devices. The prototype is a part of a research project on "Updatable and Low Power AI-Edge LSI Technology Development" commissioned by the New Energy and Industrial Technology Development Organization (NEDO) of Japan. The chip features a "quantized DNN engine" optimized for deep learning inference processing at high speeds with low power consumption. Today's edge computing devices are based on conventional, general-purpose GPUs. These processors are not generally capable of supporting the growing demand for AI-based processing requirements, such as image recognition and analysis, which need larger devices at higher cost due to increases in power consumption and heat generation.

Sign in to report inappropriate content. Video with transcript included: http://bit.ly/37yYBaD Matt Ranney explains the architecture of Uber ATG's self-driving cars and takes a look at how the software is developed, tested, and deployed. This presentation was recorded at QCon San Francisco 2019: http://bit.ly/38sivWf The next QCon is QCon London 2020 – March 2-4, 2020: http://bit.ly/2VfRldq

A sudden realization came to our minds while preparing this white paper - mobile phones are the first type of devices that received dedicated math accelerators at a pervasive scale. Such things never got wide adoption before: Intel 8087 co-processor[11], Intel Xeon Phi[2, 5] or Google TPU (Tensor Processing Unit)[6] stayed niche devices that few people use and even fewer develop for. But since the last two years, major mobile phone companies include dedicated co-processors[4] necessary for computational photography enhancement or facial recognition, that are suitable for general machine learning. Currently the dominant analytical approach stores data and runs computations in the Cloud[12]. However Cloud based methods poorly fit to a range of important practical applications including augmented reality, real-time data analysis, real-time user interaction, or processing sensitive data that incur high risks for a company if leaked, stolen or intercepted in transfer. The price of deployed analytical methods is increased by the need to have a permanently working internet connection for users, and cloud hardware rent for service providers.

On November 6th, NVIDIA introduced the latest member of the Jetson family - the Jetson Xavier NX. With the size that's smaller than a credit card, this module packs a punch. Earlier this year, NVIDIA launched Jetson Nano, the smallest yet the most powerful GPU-based edge computing device. Jetson Xavier NX, much-advanced edge computing device, has the pin compatibility with Jetson Nano making it possible to port the AIoT applications deployed on the Nano. It also supports all major AI frameworks, including TensorFlow, PyTorch, MXNet, Caffe and others.

In 2016, I visited the CEBIT conference in Hannover. It was full of so called'smart' things which I did not find smart at all. This'smart' things hype included, in fact, many devices which were simply'connected' and which, in most cases, delivered a narrowly defined single purpose benefit to the user. However, one very special presentation at CEBIT influenced my views on how AI might be delivered in the future. IBM presented a research project called SyNAPSE, developing an AI chip named'TrueNorth' which could deliver computing power equivalent to the brain of an ant while consuming just 73mW of energy.

In 2016, I visited the CEBIT conference in Hannover. It was full of so called'smart' things which I did not find smart at all. This'smart' things hype included, in fact, many devices which were simply'connected' and which, in most cases, delivered a narrowly defined single purpose benefit to the user. However, one very special presentation at CEBIT influenced my views on how AI might be delivered in the future. IBM presented a research project called SyNAPSE, developing an AI chip named'TrueNorth' which could deliver computing power equivalent to the brain of an ant while consuming just 73mW of energy.

Engineers at the Advanced Manufacturing Research Centre's Factory 2050 in Sheffield, UK are using Artificial Intelligence (AI) to learn what machine utilization looks like on the workshop floor. The aim is to create a demonstrator to show just how accessible Industry 4.0 technologies are, and how they can potentially revolutionize shop-floor productivity. The demonstrator will be the first created under an emerging AI strategy being produced at Factory 2050, which seeks to harness the innovative work being done with AI and machine learning techniques across the Advanced Manufacturing Research Centre (AMRC) and provide real use-cases for these techniques in industrial environments. "Using edge computing devices retrofitted to CNC machines, we have collected power consumption data during the production of automotive suspension components," said Rikki Coles, AI Project Engineer for the AMRC's Integrated Manufacturing Group at Factory 2050. "It isn't a complicated parameter to measure on a CNC machine, but using AI and machine learning, we can actually do a lot with such simple data."

As the adoption of artificial intelligence (AI), deep learning, and big data analytics continues to grow, it is becoming increasingly important for edge computing systems to process large data sets in a timely and efficient manner. The basic compute, storage and networking capabilities are all present today at the edge, but speeds and capacity will only continue to increase and advancements like NVMe (Non Volatile Memory Express) will offer significant performance advantages and boost AI adoption at the edge. It is possible, and becoming easier, to run AI and machine learning with analytics at the edge today, depending on the size and scale of the edge site and the particular system being used. While edge site computing systems are much smaller than those found in central data centers, they have matured, and now successfully run many workloads due to an immense growth in the processing power of today's x86 commodity servers. It's quite amazing how many workloads can now run successfully at the edge.