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Baidu open-sources Paddle Quantum toolkit for AI quantum computing research

May 27, 2020 Big Data

Baidu today announced Paddle Quantum, an open source machine learning toolkit designed to help data scientists train and develop AI within quantum computing applications. It’s built atop Baidu’s PaddlePaddle deep learning platform, and Baidu claims it’s “more flexible” compared with other quantum computing suites, reducing the complexity of one popular algorithm — quantum approximate optimization algorithm (QAOA) — by a claimed 50%.

Experts believe that quantum computing, which at a high level entails the use of quantum-mechanical phenomena like superposition and entanglement to perform computation, could one day accelerate AI workloads compared with classical computers. Moreover, AI continues to play a role in cutting-edge quantum computing research. Baidu is aiming to target researchers on both sides of the equation with Paddle Quantum — toolkits that include quantum development resources, optimizers, and quantum chemistry libraries.

Paddle Quantum supports three quantum applications — quantum machine learning, quantum chemical simulation, and quantum combinatorial optimization — and developers can use it to build quantum models from scratch or by following step-by-step instructions. It includes resources addressing challenges like combinatorial optimization problems and quantum chemistry simulations, as well as complex variable definitions and matrix multiplications enabling quantum circuit models and general quantum computing. It also features an implementation of QAOA that translates into a quantum neural network by identifying a model through classical simulation or running directly on a quantum computer.

“Since Baidu announced the establishment of [the] Institute for Quantum Computing in March 2018, one of our primary goals [has been] to build bridges between quantum computing and AI,” Baidu said in a statement. “[Paddle Quantum] … can help scientists and developers quickly build and train quantum neural network models and provide advanced quantum computing applications.”

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Today Baidu also unveiled the latest version of its PaddlePaddle machine learning framework, which over the past few months has gained 39 new algorithms for a total of 146 and more than 200 pretrained models. Among them are Paddle.js, a deep learning JavaScript library that allows developers to embed AI within web browsers or programs in apps like Baidu App and WeChat; Parakeet, a text-to-speech toolkit with cutting-edge algorithms like Baidu’s latest proposed WaveNet model; Paddle Large Scale Classification Tools (PLSC), which enables image classification model training across graphics cards; and EasyData, a new drag-and-drop data service for data collection, labeling, cleaning, and enhancement.

Over 1.9 million developers now use PaddlePaddle, according to Baidu, and 84,000 enterprises have created more than 230,000 models with the framework since its debut — up from 65,000 enterprises and 169,000 models as of last November. (PaddlePaddle, which was originally developed by Baidu scientists for the purpose of applying AI to products internally, was open-sourced in September 2016.) The company anticipates growth will accelerate in light of the recently relaunched PaddlePaddle hardware ecosystem initiative, which will see manufacturers such as Intel, Nvidia, Arm China, Huawei, MediaTek, Cambricon, Inspur, and Graphcore contribute expertise and promote AI app development.

The unveiling of Paddle Quantum follows the release earlier this year of Google’s TensorFlow Quantum, a machine learning framework that can construct quantum data sets, prototype hybrid quantum and classic machine learning models, support quantum circuit simulators, and train discriminative and generative quantum models. Facebook’s PyTorch has its own project for quantum computing in PennyLane, a library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.

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Big Data – VentureBeat

IonQ CEO Peter Chapman on how quantum computing will change the future of AI

May 10, 2020 Big Data

Businesses eager to embrace cutting-edge technology are exploring quantum computing, which depends on qubits to perform computations that would be much more difficult, or simply not feasible, on classical computers. The ultimate goals are quantum advantage, the inflection point when quantum computers begin to solve useful problems, and quantum supremacy, when a quantum computer can solve a problem that classical computers practically cannot. While those are a long way off (if they can even be achieved), the potential is massive. Applications include everything from cryptography and optimization to machine learning and materials science.

As quantum computing startup IonQ has described it, quantum computing is a marathon, not a sprint. We had the pleasure of interviewing IonQ CEO Peter Chapman last month to discuss a variety of topics. Among other questions, we asked Chapman about quantum computing’s future impact on AI and ML.

Strong AI

The conversation quickly turned to Strong AI, or Artificial General Intelligence (AGI), which does not yet exist. Strong AI is the idea that a machine could one day understand or learn any intellectual task that a human being can.

“AI in the Strong AI sense, that I have more of an opinion just because I have more experience in that personally,” Chapman told VentureBeat. “And there was a really interesting paper that just recently came out talking about how to use a quantum computer to infer the meaning of words in NLP. And I do think that those kinds of things for Strong AI look quite promising. It’s actually one of the reasons I joined IonQ. It’s because I think that does have some sort of application.”

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In a follow-up email, Chapman expanded on his thoughts. “For decades it was believed that the brain’s computational capacity lay in the neuron as a minimal unit,” he wrote. “Early efforts by many tried to find a solution using artificial neurons linked together in artificial neural networks with very limited success. This approach was fueled by the thought that the brain is an electrical computer, similar to a classical computer.”

“However, since then, I believe we now know, the brain is not an electrical computer, but an electrochemical one,” he added. “Sadly, today’s computers do not have the processing power to be able to simulate the chemical interactions across discrete parts of the neuron, such as the dendrites, the axon, and the synapse. And even with Moore’s law, they won’t next year or even after a million years.”

Chapman then quoted Richard Feynman, who famously said “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical, and by golly it’s a wonderful problem, because it doesn’t look so easy.”

“Similarly, it’s likely Strong AI isn’t classical, it’s quantum mechanical as well,” Chapman said.

Machine learning

One of IonQ’s competitors, D-Wave, argues that quantum computing and machine learning are “extremely well matched.” Chapman is still on the fence.

“I haven’t spent enough time to really understand it,” he admitted. “There clearly is a lot of people who think that ML and quantum have an overlap. Certainly, if you think of 85% of all ML produces a decision tree. And the depth of that decision tree could easily be optimized with a quantum computer. Clearly there’s lots of people that think that generation of the decision tree could be optimized with a quantum computer. Honestly, I don’t know if that’s the case or not. I think it’s still a little early for machine learning, but there clearly is so many people that are working on it. It’s hard to imagine it doesn’t have application.”

Again, in an email later, Chapman followed up. “ML has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Generally, Universal Quantum Computers excel at these kinds of problems.”

Chapman listed three improvements in ML that quantum computing will likely allow:

The level of optimization achieved will be much higher with a QC as compared to today’s classical computers.
The training time might be substantially reduced because a QC can work on the problem in parallel, where classical computers perform the same calculation serially.
The amount of permutations that can be considered will likely be much larger because of the speed improvements that QCs bring.

AI is not a focus for IonQ

Strong AI or ML, IonQ isn’t particularly interested either. The company leaves that part to its customers and future partners.

“There’s so much to be to be done in a quantum,” Champan said. “From education at one end all the way to the quantum computer itself. I think some of our competitors have taken on lots of the entire problem set. We at IonQ are just focused on producing the world’s best quantum computer for them. We think that’s a large enough task for a little company like us to handle.”

“So, for the moment we’re kind of happy to let everyone else work on different problems,” he added. “We just think, producing the world’s best quantum computer is a large enough task. We just don’t have extra bandwidth or resources to put into working on machine learning algorithms. And luckily, there’s lots of other companies that think that there’s applications there. We’ll partner with them in the sense that we’ll provide the hardware that their algorithms will run on. But we’re not in the ML business per se.”

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Big Data – VentureBeat

Folding@home crowdsourced computing project passes 1 million downloads amid coronavirus research

March 31, 2020 Big Data

Folding@home software for donating compute for medical research passed 1 million downloads, director Greg Bowman said in a tweet today. The Folding@home Consortium is made up of 11 laboratories around the world studying the molecular structure of diseases like cancer, ALS, and influenza. Research into COVID-19 started earlier this month. The crowdsourced effort is now powered by hundreds of thousands of Nvidia GPUs and tens of thousands of AMD GPUs.

There are now over 1M devices running @foldingathome ! This includes over 356K @nvidia GPUs, over 79K @AMD GPUs, and over 593K CPUs! Thanks to all our volunteers! We’re planning more blog posts on our #COVID19 work/results this week, please stay tuned.

— Greg Bowman (@drGregBowman) March 30, 2020

According to an Nvidia blog post about the milestone, nearly 400,000 gamers donated GPUs to the effort in recent days.

Last week, Folding@home said it crossed the one exaflop in compute power milestone, or collectively larger than any supercomputer ever assembled. By comparison, the Summit supercomputer at Oak Ridge National Laboratory in Tennessee has repeatedly ranked first in Top500 supercomputing ranks and is able to muster 148 petaflops of compute power.

The compute is being used to simulate “potentially druggable protein targets” and understand how the virus that causes COVID-19 interacts with the ACE2 receptor, according to the Folding@home website.

“While we will rapidly release the simulation datasets for others to use or analyze, we aim to look for alternative conformations and hidden pockets within the most promising drug targets, which can only be seen in simulation and not in static X-ray structures,” organizer John Chodera said in a March 10 blog post to launch a series of protein folding projects into production.

In another recent initiative at the intersection of gaming and medical research, earlier this month the University of Washington introduced FoldIt, a puzzle to solve protein folding challenges. In work at the intersection of protein folding and AI, Google’s DeepMind released predictions of understudied proteins associated with SARS-CoV-2 generated by the latest version of AlphaFold.

A number of open source projects are underway to accelerate progress toward a cure. Cloud computing providers in China like Alibaba, Baidu, and Tencent, as well as AWS, Google Cloud, and Azure in the U.S. are also lending compute to researchers. The CORD-19 data set is made up of tens of thousands of scholarly works and was made available last week for both medical and NLP researchers by a group including Microsoft Research, the Allen AI Institute, and the White House.

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Big Data – VentureBeat

Get 2020 vision about edge computing and 5G

December 20, 2019 Big Data

This article is part of the Technology Insight series, made possible with funding from Intel.
________________________________________________________________________________________

With 2020 predictions looming, there’s sure to be a fresh wave of hype around the edge and 5G. Now’s an ideal time to solidify and update your understanding, and explore how they’ll complement each other. If you’re processing payments, taking online orders, detecting fraud, in the financial services industry, or exploring machine learning, these two technologies can help keep you competitive in the coming months.

What and why

Edge computing is all about processing information from devices closer to where it’s being created, rather than shuttling it back and forth from the cloud. Together with 5G, computing at the edge paves the way for applications that wouldn’t have been possible before. Think augmented and virtual reality, where ultra-low latency keeps what you see in sync with what you do, or autonomous vehicles that need to make split-second decisions based on huge volumes of data.

Globally, IDC forecasts 150 billion connected devices (including RFID) by 2025, many of which will pump out data in real time. In 2017, real-time data was a mere 15% of all information created, captured, or replicated. In 2025, it’s expected to reach 30%. As a percentage, that might not sound transformational. But it’s an order of magnitude higher in raw capacity (from ~5 to ~50 zettabytes). Edge computing gives you the power to perform intelligent analysis based on that deluge of real-time data almost instantaneously, all while minimizing your bandwidth expenses.

Definition emerging

Yes, it’s nearly 2020. But the definition of “edge” still often varies depending on who you ask. From NIST to IEEE, models are still evolving. It could be the Raspberry Pi in a refrigerated big rig selectively sending sensor information to the cloud or the node processing game data for Google’s streaming Stadia platform. Although miles of proximity separate those two interpretations, they both put compute resources closer to the user.

Thanks to the collaborative, vendor-neutral 2018 State of the Edge report, there now exists a more explicit definition with some industry consensus behind it:

The edge is a location, not a thing.
There are lots of edges, but the edge we care about is the edge of the last-mile network.
This edge has two sides: an infrastructure edge and a device edge.
Compute will exist on both sides, working in coordination with the centralized cloud.

For clarity, the device edge includes end-points like phones, drones, AR headsets, IoT sensors, and connected cars; gateway devices like switches and routers; and on-premise servers. They’re all on the downstream side of your last-mile cellular or cable network. The infrastructure edge exists on the upstream side. That’s where you find your compute resources collocated with network access equipment and regional datacenters.

Get 2020 vision about edge computing and 5G

Above: Edge computing exists along a spectrum, from the device edge to the infrastructure edge.

Image Credit: Intel

In our big rig example, the Raspberry Pi is on the device edge. Rather than chewing up bandwidth by continually transmitting environmental data, it processes locally and only phones home in the event of an emergency. Conversely, hyper-local datacenters streaming a 4K gaming experience at 60 frames per second live on the infrastructure edge. Although the device edge offers a narrow latency advantage, you’re obviously going to find much more powerful hardware further upstream.

Beyond the distributed infrastructure edge lies the core network and, ultimately, the cloud, which is more centralized and scalable. But by the time you get to the cloud, latency is much higher (and far less consistent).

Benefits of low-latency edge computing

It’s easy to point at low latency as the killer application of edge computing, particularly as cloud-based software strains under the limitations of physics. Data cannot move any faster than the speed of light, so requests to servers hundreds or thousands of miles away inevitably take tens or hundreds of milliseconds to fulfill. The difference isn’t perceivable as you scroll through your Twitter feed. But those numbers wouldn’t be acceptable to a surgeon operating remotely or a gamer in virtual reality. Above all else, processing at the edge shaves away latency to keep data relevant.

Above: Today’s Internet is ill-equipped for tomorrow’s applications, which will be bandwidth-hungry and latency-sensitive.

Image Credit: State of the Edge 2020

Edge computing also saves you from shuttling every bit of data back and forth between connected devices and the cloud. If you can determine the value of information close to where it’s created, you can optimize the way it flows. Limiting traffic to just the data that belongs on the cloud cuts down on bandwidth and storage costs, even for applications that aren’t sensitive to latency.

Reliability stands to benefit from edge computing, too. A lot can go wrong between the device edge and centralized cloud. But in rugged environments like offshore platforms, refineries, or solar farms, the device and infrastructure edges can operate semi-autonomously when a connection to the cloud isn’t available.

Distributed architectures can even be a boon to security. Moving less information to the cloud means there’s less information to intercept. And analyzing data at the edge distributes risk geographically. The endpoints themselves aren’t always easy to protect, so firewalling them at the edge helps limit the scope of an attack. Further, keeping data local may be useful for compliance reasons. An edge infrastructure gives you the flexibility to limit access based on geography or copyright limitations.

The edge is pervasive; 5G makes it better

Edge computing is not new. As far back as 2000, content delivery networks were being referred to as edge networks. But it’s universally accepted that as 5G coverage grows, edge computing is going to help address the high-bandwidth, low-latency requirements of modern applications with local, rather than regional, compute resources.

The technology underlying 5G will add speed, reliability, and flexibility to enterprise applications by getting compute resources closer to where data is being created. Information will move between 5G networks efficiently, rather than requiring a trip to the centralized cloud and back. As a result, we’re going to be looking at use cases that previously weren’t possible.

According to the 2020 State of the Edge report, the largest demand for edge computing comes from communication network operators virtualizing their infrastructure and upgrading their networks for 5G. Mobile consumer services running on those networks are going to rely on edge computing to enable streaming game platforms, augmented/virtual reality, and AI.

Smart homes, smart grids, and smart cities all share a proclivity for device edge platforms. As those use cases evolve and become more sophisticated, though, there will be demand for infrastructure edge capabilities, too. 5G’s provisions for ultra-reliable low-latency communications (URLLC) and massive machine-type communication (mMTC) mean the devices and edge can be even closer together, making their short connection more efficient.

Above: Traffic lights are networked and connected to edge gateway server, where their data can be collected and analyzed. As part of an edge network, they can feed data to map tools and reroute around congestion.

Image Credit: Moor Insights & Strategy

And who could forget about the autonomous automobile, the poster child for edge computing enhanced by 5G? Modern automobiles already utilize compute resources on the device edge for collision avoidance, lane-keeping, and adaptive cruise control. But as assisted and autonomous driving features become more sophisticated, infrastructure edge resources will be required to add intelligence that could only come from the surrounding environment. Good examples: rerouting a trip based on traffic miles ahead, communicating with other autonomous vehicles to accelerate from a stoplight in unison, or making split-second decisions to avoid unsafe situations.

The edge is still young

The empowered edge is one of Gartner’s Top 10 Strategic Technology Trends for 2020. However, several other concepts on its watchlist have roots in edge computing as well. Hyperautomation, which deals with the application of artificial intelligence and machine learning to augment human input, is going to rely on a foundation of low latency and unflagging reliability. The multiexperience is another example, incorporating multisensory and multitouchpoint interfaces dependent on high bandwidth and real-time processing. Of course, autonomous things are all about AI, 5G, and edge computing.

Above: The global annual CAPEX for edge IT and data center facilities is forecast to reach $ 146 billion in 2028 with a 35 percent CAGR.

Image Credit: State of the Edge 2020

Enabling those novel use cases will require substantial investment. A forecast model by Tolaga Research predicts a cumulative CapEx spend of $ 700 billion between now and 2028 on edge IT and datacenter infrastructure. As the computing pendulum swings from the centralized cloud to a distributed edge, opportunities abound, particularly in the maturing infrastructure edge. Understanding the impact of edge computing and 5G will allow you to provide seamless customer experiences, test new markets, and act on insights in real-time.

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Big Data – VentureBeat

AWS Braket lets customers experiment with a range of quantum computing hardware

December 3, 2019 Big Data

Announcements from Amazon’s re:Invent 2019 conference in Las Vegas this week are coming in at a steady clip, most recently several relating to the tech giant’s ongoing quantum computing efforts. This afternoon, Amazon Web Services (AWS) — Amazon’s cloud computing division — detailed three key initiatives as part its plans to advance quantum computing tech: a fully managed service called Amazon Braket, a new academic partnership with the California Institute of Technology (Caltech), and a Quantum Solutions Lab.

“With quantum engineering starting to make more meaningful progress, customers are asking for ways to experiment with quantum computers and explore the technology’s potential,” said AWS senior vice president Charlie Bell. “We believe that quantum computing will be a cloud-first technology and that the cloud will be the main way customers access the hardware.”

AWS Braket

AWS Braket, which launches today in preview, is a fully managed service that provides customers a development environment to build quantum algorithms, test them on simulated quantum computers, and try them on a range of different quantum hardware architectures. Using Jupyter notebooks and existing AWS services, users can assess both present and future capabilities including quantum annealing from D-Wave, ion trap devices from IonQ, and superconducting chips from Rigetti. More will arrive in the coming months.

Amazon says partners were chosen “for their quantum technologies,” and that both customers (like Boeing) and hardware providers can design quantum algorithms using the Braket developer toolkit. They’re afforded the choice of executing either low-level quantum circuits or fully managed hybrid algorithms, and of selecting between software simulators and quantum hardware.

AWS Center for Quantum Computing

Alongside Braket, Amazon announced the AWS Center for Quantum Computing, a new laboratory to be established at Caltech with the goal of “boosting innovation in science and industry.” According to the company, it will bring together Amazon researchers and engineers with academic institutions in quantum computing to develop more powerful quantum computing hardware and identify novel quantum applications.

“We are delighted to join with our colleagues at AWS and our academic partners to address the fundamental challenges that must be overcome if quantum computing is to reach its full potential,” said provostial chair and professor of chemistry and chemical engineering David Tirrell. “Caltech has made substantial investments in both experimental and theoretical quantum science and technology over the years, and the new Center will provide an extraordinary opportunity to maximize the impact of those investments.”

AWS Quantum Solutions Lab

Lastly, Amazon debuted the Quantum Solutions Lab, which aims to connect AWS customers with quantum computing experts — including those from 1Qbit, Rahko, Rigetti, QC Ware, QSimulate, Xanadu, and Zapata — to identify ways to apply quantum computing inside their organizations. Amazon says that it and its partners will work with customers on experiments and guide them to incorporate quantum solutions into their business, in part through lab programs that combine hands-on educational workshops with brainstorming sessions.

Amazon’s trio of announcements come after Google made available to cloud customers its Bristlecone quantum processor, and after IBM began giving enterprise customers and research institutions remote access to its quantum machine. In November, another competitor — Microsoft — took the wraps off of Azure Quantum, a service that offers select partners access to three prototype quantum computers from IonQ, Honeywell, and QCI.

The quantum computing market could prove to be a lucrative new source of revenue for AWS. According to some analysts, it’ll reach nearly $ 1 billion by 2025, up from $ 89.35 million in 2016.

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Big Data – VentureBeat

ASU 2018-15 Simplifies the Process for Accounting for Cloud Computing Expenses

October 10, 2019 NetSuite

Posted by Christopher Miller, Distinguished Solution Specialist

New FASB standard offers guidance on accounting for cloud computing license costs and implementations.

I am frequently contacted by customers and our internal team about how to account for NetSuite costs. As cloud computing became more popular, businesses took different approaches to how they accounted for it on their financial statements. A few of the popular ideas were:

Treat a cloud computing arrangement as a fixed asset and depreciate it. Capitalize everything.
Present the contract as prepaid and amortize it as an intangible asset.
Amortize the contract as an expense and capitalize all of the implementation costs.
Reference the contract to lease accounting standards and follow those as an analogy for cloud computing.
Treat the cloud computing arrangement as internally developed software.

Each of these ideas had some merit. However, as the percentage of businesses using cloud products increased, the FASB and other standard setters realized that the “diversity” in practice was growing too large.

So, in 2015, the FASB issued ASU No. 2015-05, “Customer’s Accounting for Fees Paid in a Cloud Computing Arrangement,” to simplify to process. The goal was to help a business determine what kind of contract their arrangement included.

The key determination was to help a user know if their arrangement contained a software license or not. Using the guidance under ASC 985-605, customers are required to determine if their cloud arrangement contains a software license. If the answer is yes, then treat the contract as an internal use software intangible under ASC 350-40. If no, then account for the arrangement as a service contract.

Screen%20Shot%202019 10 09%20at%2010.06.26%20AM ASU 2018 15 Simplifies the Process for Accounting for Cloud Computing Expenses

The release of No. 2015-05 clarified several of the key questions presented above. If the cloud computing arrangement included a software license, then the contract was treated as internal-use software. This allowed the purchaser to capitalize the license and related implementation costs. It also clarified that when an arrangement didn’t have a software license embedded, that it was a service contract and should be treated as an operating expense. This clarification put an end to treating service contracts as fixed assets, etc.

However, ASU 2015-5 didn’t determine the process for handling the implementation costs of a service contract. Many of the same questions existed, such as whether you can capitalize implementation costs, or must they all be expensed?

To clarify the process of handling service contract implementations, FASB issued ASU No. 2018-14, effective for all filers after Dec 15, 2019.

Key Provision 1 – Apply Internal Use Software Guidance

When entering into a cloud computing contract that is a service contract, entities will now apply the same guidance toward implementation costs that are used for internal use software. Here are the key guidelines to be aware of when applying this new guidance:

Project costs to purchase, develop or install the software generally can be capitalized.
Activities related to testing, customization, configuration and scripting can be capitalized.
Training and data conversion costs are to be expensed as incurred.
These costs can be both internal and external, such as payroll, contractors and travel expenses.

Key considerations here are to consider how you define the different terms and the quality of your project timekeeping/management to identify the type of work performed.

Amortization of Implementation Costs

ASU 2018-15 states that implementation costs should be amortized over the term of the associated cloud computing arrangement service on a straight-line basis. In addition, it states that the usage rate (number of transactions, users, data throughput) should not be used as a basis for amortization.

In section 350-40-35-14 the guidance states additional periods to be included in the amortization period:

An entity (customer) shall determine the term of the hosting arrangement that is a service contract as the fixed noncancellable term of the hosting arrangement plus all of the following:
Periods covered by an option to extend the hosting arrangement if the entity (customer) is reasonably certain to exercise that option
Periods covered by an option to terminate the hosting arrangement if the entity (customer) is reasonably certain not to exercise that option
Periods covered by an option to extend (or not to terminate) the hosting arrangement in which exercise of the option is controlled by the vendor.
When reassessing the amortization term, an entity shall consider the following to reassess the term:
Obsolescence
Technology
Competition
Other economic factors
Rapid changes that may be occurring in the development of hosting arrangements or hosted software

Presentation

The new ASU makes a few critical changes to the presentation of cloud service contracts. A few highlights include:

350-40-45-2 An entity shall present the capitalized implementation costs described in paragraph 350-40-25-18 in the same line item in the statement of financial position that a prepayment of the fees for the associated hosting arrangement would be presented.
350-40-45-1 An entity shall present the amortization of implementation costs described in paragraph 350-40-35-13 in the same line item in the statement of income as the expense for fees for the associated hosting arrangement.
350-40-45-3 An entity shall classify the cash flows from capitalized implementation costs described in paragraph 350-40-25-18 in the same manner as the cash flows for the fees for the associated hosting arrangement.

The key takeaway for presentation in the new guidance is that no costs associated with a CCA Service Contract should be treated as depreciation or amortization and should all be included in operating income.

Disclosure Requirements

There are a few key disclosures required from the new ASU. Specifically they are:

The nature of its arrangements for cloud computing.
Any amortization expenses for the period
Major classes of implementation costs that have been capitalized
Accumulated amortization of the implementation costs

Conclusion

With the release of ASU 2018-15, the way to manage cloud computing contracts has clarity. It is now a three-step process that consists of determining whether an arrangement has a software license included. Manage the implementation project and capitalize the correct costs. Lastly present the costs correctly based on the new ASU.

Posted on Wed, October 9, 2019
by NetSuite filed under

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The NetSuite Blog

Nvidia will support Arm hardware for high-performance computing

June 17, 2019 Big Data

At the International Supercomputing Conference (ISC) in Frankfurt, Germany this week, Santa Clara-based chipmaker Nvidia announced that it will support processors architected by British semiconductor design company Arm. Nvidia anticipates that the partnership will pave the way for supercomputers capable of “exascale” performance — in other words, capable of completing at least a quintillion floating point computations (“flops”) per second, where a flop equals two 15-digit numbers multiplied together.

Nvidia says that by 2020 it will contribute its full stack of AI and high-performance computing (HPC) software to the Arm ecosystem, which by Nvidia’s estimation now accelerates over 600 HPC applications and machine learning frameworks. Among other resources and services, it will make available CUDA-X AI and HPC libraries, graphics-accelerated frameworks, software development kits, PGI compilers with OpenACC support, and profilers.

Nvidia founder and CEO Jensen Huang pointed out in a statement that, thanks to this commitment, Nvidia will soon accelerate all major processor architectures: x86, IBM’s Power, and Arm.

“As traditional compute scaling has ended, the world’s supercomputers have become power constrained,” said Huang. “Our support for Arm, which designs the world’s most energy-efficient CPU architecture, is a giant step forward that builds on initiatives Nvidia is driving to provide the HPC industry a more power-efficient future.”

This is hardly Nvidia’s first collaboration with Arm. The former’s AGX platform incorporates Arm-based chips, and its Deep Learning Accelerator (NVDLA) — a modular, scalable architecture based on Nvidia’s Xavier system-on-chip — integrates with Arm’s Project Trillium, a platform that aims to bring deep learning inferencing to a broader set of mobile and internet of things (IoT) devices.

If anything, today’s news highlights Nvidia’s concerted push into an HPC market that’s forecast to be worth $ 59.65 billion by 2025. To this end, the chipmaker recently worked with InfiniBand and ethernet interconnect supplier Mellanox to optimize processing across supercomputing clusters, and it continues to invest heavily in 3D packaging techniques and interconnect technology (like NVSwitch) that allow for dense scale-up nodes.

“We have been a pioneer in using Nivida [graphics cards] on large-scale supercomputers for the last decade, including Japan’s most powerful ABCI supercomputer,” said Satoshi Matsuoka, director at Riken, a large scientific research institute in Japan. “At Riken R-CCS [Riken Center for Computational Science], we are currently developing the next-generation, Arm-based exascale Fugaku supercomputer and are thrilled to hear that Nvidia’s GPU acceleration platform will soon be available for Arm-based systems.”

Nvidia has notched a few wins already. Last fall, the TOP500 ranking of supercomputer performance (based on LINPACK score) showed a 48% jump year-over-year in the number of systems using the company’s GPU accelerators, with the total number climbing to 127, or 3 times greater than five years prior. Two of the world’s fastest supercomputers made the list — the U.S. Department of Energy’s Summit at Oak Ridge National Laboratory and Sierra at Lawrence Livermore National Lab — and others featured Nvidia’s DGX-2 Pod, which combines 36 DGX-2 systems and delivers more than 3 petaflops of double-precision performance.

DGX-2 was announced in March 2018 at Nvidia’s GPU Technology Conference in Santa Clara and boasts 300 processors capable of delivering two petaflops of computational power while occupying only 15 racks of datacenter space. It complements HGX-2, a cloud server platform equipped with 16 Tesla V100 graphics processing units that collectively provide half a terabyte of memory and two petaflops of compute power.

DGX SuperPod

Alongside the partnership announcement this moring, Nvidia revealed what it claims is the world’s 22nd-fastest supercomputer: the DGX SuperPod. VP of AI infrastructure Clement Farabet says it’ll accelerate the company’s autonomous vehicle development.

Nvidia will support Arm hardware for high performance computing

Above: The Nvidia DGX SuperPod.

Image Credit: Nvidia

“AI leadership demands leadership in compute infrastructure,” said Farabet. “Few AI challenges are as demanding as training autonomous vehicles, which requires retraining neural networks tens of thousands of times to meet extreme accuracy needs. There’s no substitute for massive processing capability like that of the SuperPod.”

The SuperPod contains 96 DGX-2H units and 1,536 V100 Tensor Core graphics chips in total, interconnected with Mellanox and Nvidia’s NVSwitch technologies. It’s about 400 times smaller than comparable top-ranked supercomputing systems and it takes as little as three weeks to assemble while delivering 9.4 petaflops of computing performance. In real-world tests, it managed to train the benchmark AI model ResNet-50 in less than two minutes.

Customers can buy the SuperPod in whole or in part from any of Nvidia’s DGX-2 partners.

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Big Data – VentureBeat

Baidu unveils open source edge computing platform and AI boards

January 11, 2019 Big Data

Baidu’s had a busy week. Fresh off of yesterday’s unveiling of Apollo 3.5, the latest generation of its self-driving platform, the Beijing company announced OpenEdge, an open source computing platform that enables developers to build edge applications “with more flexibility.” It also announced two new AI hardware development platforms: the BIE-AI-Box, a kit for in-car video analysis designed in partnership with Intel, and the BIE-AI-Board, a chipboard codeveloped with NXP that’s optimized for object classification.

“The explosive growth of IoT devices and rapid adoption of AI is fueling great demand for edge computing,” Watson Yin, vice president and general manager of Baidu Cloud, said. “Edge computing is a critical component of Baidu’s ABC (AI, Big Data and Cloud Computing) strategy.”

OpenEdge

OpenEdge is the local package component of Baidu’s commercial Baidu Intelligent Edge (BIE), which the company claims is China’s first open source edge computing platform.

The BIE platform underpinning OpenEdge offers a cloud-based management suite to manage edge nodes, edge apps, and resources such as certification, password, and program code. It supports models trained on AI frameworks such as Google’s TensorFlow and Baidu’s own PaddlePaddle, meaning that developers can train AI models on BIE and deploy them locally. Moreover, devices deployed with BIE are afforded additional features, like the ability to cache data and perform on-device processing in the event of a flaky network connection.

These tools together, Baidu says, let developers build custom edge computing systems on a range of hardware that can collect data, distribute messages, perform AI inference, and synchronize with the cloud.

“By moving the compute closer to the source of the data, it greatly reduces the latency, lowers the bandwidth usage, and ultimately brings real-time and immersive experiences to end users,” Yin said. “And by providing an open source platform, we have also greatly simplified the process for developers to create their own edge computing applications.”

BIE-AI-Box and BIE-AI-Board

Baidu and Intel’s BIE-AI-Box is, as alluded to earlier, a hardware kit custom built for analyzing the frames captured by cockpit cameras. Toward that end, it incorporates BIE technologies “specially” engineered for the purpose, and connects with cameras for road recognition, car body monitoring, driver behavior recognition, and other tasks.

As for the BIE-AI-Board, which is designed for object recognition, it’s compact enough to be embedded into cameras, drones, robots, and other hardware. Early partners have integrated it with electric vehicles to assess the health of chargers and with agricultural drones to analyze crop spectral data, Baidu says. (In the latter case, it helped to reduce pesticide use by up to 50 percent.)

Baidu’s looking to the cloud for revenue growth. It recently partnered with Nvidia to bring the chipmaker’s Volta graphics platform to Baidu Cloud, and in July 2018, it unveiled two new chips for AI workloads: the Kunlun 818-300 for machine learning model training and the Kunlun 818-100 for inference.

According to Gartner, the cloud computing market is projected to be worth $ 441 billion by 2020.

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Voice: The Next Big Breakthrough In Enterprise Computing

August 26, 2018 BI News and Info

Voice is set to be the biggest enterprise tech disruption since the smartphone — if we can overcome the remaining practical difficulties.

Over the decades, computer interaction has slowly become more natural, with the computers adapting to users rather than the other way around, with the slow progression from punch cards to keyboards and computer mouse devices.

Currently, touch interfaces are the default standard in almost any device that includes a screen, from cameras to cars (and they’re habit-forming! Am I the only one who occasionally tries to swipe TV screens or pinch-to-zoom in on paper images?). Now it’s time for the inevitable big step: talking to your corporate device. In the movies, at least, we’ve been talking to computers for a long time, from Jarvis in “Iron Man” to this famous scene in “Star Trek IV,” where Scotty tries to issue instructions to a 1980s computer:

[embedded content]

Of course, voice assistants have been available in mobile phones for years — Apple’s Siri came out in 2011. But they have had limited impact on corporate environments so far.

What’s changing is that advances in computing power and machine learning have enabled computers to transcribe speech better than human beings, and then accurately interpret the result, without cumbersome coding. And the new systems can more efficiently update themselves, learning from their mistakes rather than requiring explicit instruction.

The number of voice-enabled devices continues to skyrocket — over 50 million of them are expected to be sold this year and the global voice tech industry is expected to reach US$ 126.5 billion by 2023.

There are real benefits to voice interfaces for workers, especially compared to fiddly mobile keyboards. A study earlier this year by researchers from Stanford University, the University of Washington, and Baidu USA found that voice input with mobile devices was nearly three times faster than typing, will little difference in the error rate between the two types of input.

One of the biggest benefits of voice interfaces is that they can provide a “universal remote control” to enterprise business systems. Google has already demonstrated that voice can be used to connect computers to people-based systems, for example setting up an appointment at the hair salon:

[embedded content]

These kinds of connections can make even more sense in the corporate world. Every large organization has many different applications, and providing a coherent workflow across them has always required coding, which can be slow and expensive. But if the systems are all voice-enabled, workers can easily switch from one to another without requiring any explicit integration.

In addition, it’s likely that enterprise systems will start talking to each other. It’s very difficult to implement universal standards in computing, and using voice — although obviously inefficient — could be a very pragmatic short-term solution for system integration. We’re already seeing this in the consumer world, with the recent news that Amazon’s Alexa can now “talk to” Microsoft’s Cortana and vice-versa.

All these advances mean that corporate digital assistants are going mainstream, providing chat and voice interfaces to a full range of corporate business activities, including easy access to HR information like outstanding vacation days:

[embedded content]

Voice is also becoming part of innovative service approaches. For example, Workheld provides innovative field management systems to increase the productivity of construction- and service processes. The company uses machine learning and text analytics technology to automatically match customer jobs with the most appropriate technician, with hands-free voice interfaces to help workers through maintenance steps.

There are still some problems with voice interfaces — for example, it can be hard (or just embarrassing) to use in a busy office where there’s a lot of background noise. As ever, technology companies are working on solutions: for example, more advanced noise cancellation using AI, picking out your voice in a crowd, reading your lips, or even detecting your words directly through your jaw, without you having to say them out loud (so maybe the future is actually about voiceless voice interfaces?!).

There are also clear dangers. For example, all those microphones provide ample opportunities for nefarious eavesdropping and personal tracking (from George Orwell’s 1984: “there was always the danger of concealed microphones by which your voice might be picked up and recognized “)

And how will corporate security be enforced with voice interfaces when algorithms can create uncannily accurate synthesized speech? There are already problems with corporate “vishing” today — what will happen when the distinctive (but faked) voice of your CEO connects to the system to request an exceptional wire transfer? (Or when your teenage daughter calls you to say she’s in trouble and needs money?!).

Overall, I expect there to be huge leaps in voice adoption in the enterprise this year, primarily through optional voice interaction with chatbot interfaces. You can read more in this Digitalist Magazine Executive Quarterly article I helped co-author: “Say What?!”

And then we can all look forward to the next big interface wave in the enterprise: artificial and augmented reality.

This article originally appeared in Digital Business & Business Analytics.

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This article is part of the Technology Insight series, made possible with funding from Intel. ________________________________________________________________________________________

What and why

Definition emerging

Benefits of low-latency edge computing

The edge is pervasive; 5G makes it better

The edge is still young

AWS Braket

AWS Center for Quantum Computing

AWS Quantum Solutions Lab

DGX SuperPod

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Recent Posts

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This article is part of the Technology Insight series, made possible with funding from Intel.
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