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AMD CTO: ‘AI across our entire portfolio’

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AMD CTO: ‘AI across our entire portfolio’

In a presentation for industry analysts, AMD chief technology officer Mark Papermaster laid out the company’s vision for artificial intelligence everywhere — from PC and edge endpoints to the largest hypervisor servers.

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The current buildout of the artificial intelligence infrastructure is an event as big as the original launch of the internet.

AI, now mainly an expense, will soon be monetized. Thousands of AI applications are coming.

And AMD plans to embed AI across its entire product portfolio. That will include components and software on everything from PCs and edge sensors to the largest servers used by the big cloud hypervisors.

These were among the comments of Mark Papermaster, AMD’s executive VP and CTO, during a recent fireside chat hosted by stock research firm Arete Research. During the hour-long virtual presentation, Papermaster answered questions from moderator Brett Simpson of Arete and attending stock analysts. Here are the highlights.

The overall AI market

AMD has said it believes the total addressable market (TAM) for AI through 2027 is $400 billion. “That surprised a lot of people,” Papermaster said, but AMD believes a huge AI infrastructure is needed.

That will begin with the major hyperscalers. AWS, Google Cloud and Microsoft Azure are among those looking at massive AI buildouts.

But there’s more. AI is not only in the domain of these massive clusters. Individual businesses will be looking for AI applications that can drive productivity and enhance the customer experience.

The models for these kinds of AI systems are typically smaller. They can be run on smaller clusters, too, whether on-premises or in the cloud.

AI will also make its way into endpoint devices. They’ll include PCs, embedded devices, and edge sensors.

Also, AI is more than just compute. AI systems also require robust memory, storage and networking.

“We’re thrilled to bring AI across our entire product portfolio,” Papermaster said.

Looking at the overall AI market, AMD expects to see a compound annual growth rate of 70%. “I know that seems huge,” Papermaster said. “But we are investing to capture that growth.”

AI pricing

Pricing considerations need to take into account more than just the price of a GPU, Papermaster argued. You really have to look at the total cost of ownership (TCO).

The market is operating with an underlying premise: Demand for AI compute is insatiable. That will drive more and more compute into a smaller area, delivering more efficient power per FLOP, the most common measure of AI compute performance.

Right now, the AI compute model is dominated by a single player. But AMD is now bringing the competition. That includes the recently announced MI300 accelerator. But as Papermaster pointed out, there’s more, too. “We have the right technology for the right purpose,” he said.

That includes using not only GPUs, but also (where appropriate) CPUs. These workloads can include AI inference, edge computing, and PCs. In this way, user organizations can better manage their overall CapEx spend.

As moderator Simpson reminded him, Papermaster is fond of saying that customers buy road maps. So naturally he was asked about AMD’s plans for the AI future. Papermaster mainly deferred, saying more details will be forthcoming. But he also reminded attendees that AMD’s investments in AI go back several years and include its ROCm software enablement stack.

Training vs. inference

Training and inference are currently the two biggest AI workloads. Papermaster believes we’ll see the AI market bifurcate along their two lines.

Training depends on raw computational power in a vast cluster. For example, the popular ChatGPT generative AI tool uses a model with over a trillion parameters. That’s where AMD’s MI300 comes into play, Papermaster said, “because it scales up.”

This trend will continue, because for large language models (LLMs), the issue is latency. How quickly can you get a response? That requires not only fast processors, but also equally fast memory.

More specific inferencing applications, typically run after training is completed, are a different story, Papermaster said, adding: “Essentially, it’s ‘I’ve trained my model; now I want to organize it.’” These workloads are more concise and less demanding of both power and compute, meaning they can run on more affordable GPU-CPU combinations.

Power needs for AI

User organizations face a challenge: While running an AI system requires a lot of power, many data centers are what Papermaster called “power-gated.” In other words, they’re unable to drive up compute capacity to AI levels using current technology.

AMD is on the case. In 2020, the company committed itself to driving a 30x improvement in power efficiency for its products by 2025. Papermaster said the company is still on track to deliver that.

To do so, he added, AMD is thinking in terms of “holistic design.” That means not just hardware, but all the way through an application to include the entire stack.

One promising area involves AI workloads that can use AI approximation. These are applications that, unlike HPC workloads, do not need incredible levels of accuracy. As a result, performance is better for lower-precision arithmetic than it is for high-precision. “Not all AI models are created equally,” Papermaster said. “You’ll need smaller models, too.”

AMD is among those who have been surprised by the speed of AI adoption. In response, AMD has increased its projection of AI sales this year from $2 billion to $3.5 billion, what Papermaster called the fastest ramp AMD has ever seen.

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Looking to accelerate AI? Start with the right mix of storage

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Looking to accelerate AI? Start with the right mix of storage

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That’s right, storage might be the solution to speeding up your AI systems.

Why? Because today’s AI and HPC workloads demand a delicate storage balance. On the one hand, they need flash storage for high performance. On the other, they also need object storage for data that, though large, is used less frequently.

Supermicro and AMD are here to help with a reference architecture that’s been tested and validated at customer sites.

Called the Scale-Out Storage Reference Architecture, it offers a way to deliver massive amounts of data at high bandwidth and low latency to data-intensive applications. The architecture also defines how to manage data life-cycle concerns, including migration and cold-storage retention.

At a high level, Supermicro’s reference architecture address three important demands for AI and HPC storage:

  • Data lake: It needs to be large enough for all current and historical data.
  • All-flash storage tier: Caches input for application servers and deliver high bandwidth to meet demand.
  • Specialized application servers: Offering support that ranges from AMD EPYC high-core-count CPUs to GPU-dense systems.

Tiers for less tears

At this point, you might be wondering how one storage system can provide both high performance and vast data stores. The answer: Supermicro’s solution offers a storage architecture in 3 tiers:

  • All flash: Stores active data that needs the highest speeds of storage and access. This typically accounts for just 10% to 20% of an organization’s data. For the highest bandwidth networking, clusters are connected with either 400 GbE or 400 Gbps InfiniBand. This tier is supported by the Weka data platform, a distributed parallel file system that connects to the object tier.
  • Object: Long-term, capacity-optimized storage. Essentially, it acts as a cache for the application tier. These systems offer high-density drives with relatively low bandwidth and networking typically in the 100 GbE range. This tier managed by Quantum ActiveScale Object Storage Software, a scalable, always-on, long-term data repository.
  • Application: This is where your data-intensive workloads, such as machine-learning training, reside. This tier uses 400 Gbps InfiniBand networking to access data in the all-flash tier.

What’s more, the entire architecture is modular, meaning you can adjust the capacity of the tiers depending on customer needs. This can also be adjusted to deploy different kinds of products — for example, open-source vs. commercial software.

To give you an idea of what’s possible, here’s a real-life example. One of the world’s largest semiconductor makers has deployed the Supermicro reference architecture. Its goal: use AI to automate the detection of chip-wafer defects. Using the reference architecture, the company was able to fill a software installation with 25 PB of data in just 3 weeks, according to Supermicro.

Storage galore

Supermicro offers more than just the reference architecture. The company also offers storage servers powered by the latest AMD EPYC processors. These servers can deliver flash storage that is ideal for active data. And they can handle high-capacity storage on physical discs.

That includes the Supermicro Storage A+ Server ASG-2115S-NE332R. It’s a 2U rackmount device powered by an AMD EPYC 9004 series processor with 3D V-Cache technology.

This storage server has 32 bays for E3.S hot-swap NVM3 drives. (E3.S is a form factor designed to optimize the flash density of SSD drives.) The server’s total storage capacity comes to an impressive 480 TB. It also offers native PCIe 5 performance.

Of course, every organization has unique workloads and requirements. Supermicro can help you here, too. Its engineering team stand ready to help you size, design and implement a storage system optimized to meet your customers’ performance and capacity demands.

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AMD Instinct MI300 Series: Take a deeper dive in this advanced technology

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AMD Instinct MI300 Series: Take a deeper dive in this advanced technology

Take a look at the innovative technology behind the new AMD Instinct MI300 Series accelerators.

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Earlier this month, AMD took the wraps off its highly anticipated AMD Instinct MI300 Series of generative AI accelerators and data-center acceleration processing units (APUs). During the announcement event, AMD president Victor Peng said the new components had been “designed with our most advanced technologies.”

Advanced technologies indeed. With the AMD Instinct MI300 Series, AMD is writing a brand-new chapter in the story of AI-adjacent technology.

Early AI developments relied on the equivalent of a hastily thrown-together stock car constructed of whichever spare parts happened to be available at the time. But those days are over.

Now the future of computing has its very own Formula 1 race car. It’s extraordinarily powerful and fine-tuned to nanometer tolerances.

A new paradigm

At the heart of this new accelerator series is AMD’s CDNA 3 architecture. This third generation employs advanced packaging that tightly couples CPUs and GPUs to bring high-performance processing to AI workloads.

AMD’s new architecture also uses 3D packaging technologies that integrate up to 8 vertically stacked accelerator complex dies (XCDs) and four I/O dies (IODs) that contain system infrastructure. The various systems are linked via AMD Infinity Fabric technology and are connected to 8 stacks of high-bandwidth memory (HBM).

High-bandwidth memory can provide far more bandwidth and yet much lower power consumption compared with the GDDR memory found in standard GPUs. Like many of AMD’s notable innovations, its HBM employs a 3D design.

In this case, the memory modules are stacked vertically to shorten the distance the data needs to travel. This also allows for smaller form factors.

AMD has implemented the HMB using a unified memory architecture. This is an increasingly popular design in which a single array of main-memory modules supports both the CPU and GPU simultaneously, speeding tasks and applications.

Unified memory is more efficient than traditional memory architecture. It offers the advantage of faster speeds along with lower power consumption and ambient temperatures. Also, data need not be copied from one set of memory to another.

Greater than the sum of its parts

What really makes AMD CDNA 3 unique is its chiplet-based architecture. The design employs a single logical processor that contains a dozen chiplets.

Each chiplet, in turn, is fabricated for either compute or memory. To communicate, all the chiplets are connected via the AMD Infinity Fabric network-on-chip.

The primary 5nm XCDs contain the computational elements of the processor along with the lowest levels of the cache hierarchy. Each XCD includes a shared set of global resources, including the scheduler, hardware queues and 4 asynchronous compute engines (ACE).

The 6nm IODs are dedicated to the memory hierarchy. These chiplets carry a newly redesigned AMD Infinity Cache and an HBM3 interface to the on-package memory. The AMD Infinity Cache boosts generational performance and efficiency by increasing cache bandwidth and reducing the number of off-chip memory accesses.

Scaling ever upward

System architects are constantly in the process of designing and building the world’s largest exascale-class supercomputers and AI systems. As such, they are forever reaching for more powerful processors capable of astonishing feats.

The AMD CDNA 3 architecture is an obvious step in the right direction. The new platform takes communication and scaling to the next level.

In particular, the advent of AMD’s 4th Gen Infinity Architecture Fabric offers architects a new level of connectivity that could help produce a supercomputer far more powerful than anything we have access to today.

It’s reasonable to expect that AMD will continue to iterate its new line of accelerators as time passes. AI research is moving at a breakneck pace, and enterprises are hungry for more processing power to fuel their R&D.

What will researchers think of next? We won’t have to wait long to find out.

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Supermicro debuts 3 GPU servers with AMD Instinct MI300 Series APUs

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Supermicro debuts 3 GPU servers with AMD Instinct MI300 Series APUs

The same day that AMD introduced its new AMD Instinct MI300 series accelerators, Supermicro debuted three GPU rackmount servers that use the new AMD accelerated processing units (APUs). One of the three new systems also offers energy-efficient liquid cooling.

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Supermicro didn’t waste any time.

The same day that AMD introduced its new AMD Instinct MI300 series accelerators, Supermicro debuted three GPU rackmount servers that use the new AMD accelerated processing units (APUs). One of the three new systems also offers energy-efficient liquid cooling.

Here’s a quick look, plus links for more technical details:

Supermicro 8-GPU server with AMD Instinct MI300X: AS -8125GS-TNMR2

This big 8U rackmount system is powered by a pair of AMD EPYC 9004 Series CPUs and 8 AMD Instinct MI300X accelerator GPUs. It’s designed for training and inference on massive AI models with a total of 1.5TB of HBM3 memory per server node.

The system also supports 8 high-speed 400G networking cards, which provide direct connectivity for each GPU; 128 PCIe 5.0 lanes; and up to 16 hot-swap NVMe drives.

It’s an air-cooled system with 5 fans up front and 5 more in the rear.

Quad-APU systems with AMD Instinct MI300A accelerators: AS -2145GH-TNMR and AS -4145GH-TNMR

These two rackmount systems are aimed at converged HPC-AI and scientific computing workloads.

They’re available in the user’s choice of liquid or air cooling. The liquid-cooled version comes in a 2U rack format, while the air-cooled version is packaged as a 4U.

Either way, these servers are powered by four AMD Instinct MI300A accelerators, which combine CPUs and GPUs in an APU. That gives each server a total of 96 AMD ‘Zen 4’ cores, 912 compute units, and 512GB of HBM3 memory. Also, PCIe 5.0 expansion slots allow for high-speed networking, including RDMA to APU memory.

Supermicro says the liquid-cooled 2U system provides a 50%+ cost savings on data-center energy. Another difference: The air-cooled 4U server provides more storage and an extra 8 to 16 PCIe acceleration cards.

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AMD drives AI with Instinct MI300X, Instinct MI300A, ROCm 6

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AMD drives AI with Instinct MI300X, Instinct MI300A, ROCm 6

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AMD this week formally introduced its AMD Instinct MI300X and AMD Instinct MI300A accelerators, two important elements of the company’s new push into AI.

During the company’s two-hour “Advancing AI” event, held live in Silicon Valley and live-streamed on YouTube, CEO Lisa Su asserted that “AI is absolutely the No. 1 priority at AMD.”

She also said that AI is both “the future of computing” and “the most transformative technology of the last 50 years.”

AMD is leading the AI charge with its Instinct MI300 Series accelerators, designed for both cloud and enterprise AI and HPC workloads. These systems offer GPUs, large and fast memory, and 3D packaging using the 4th gen AMD Infinity Architecture.

AMD is also relying heavily on cloud, OEM and software partners that include Meta, Microsoft and Oracle Cloud. Another partner, Supermicro, announced additions to its H13 generation of accelerated servers powered by 4th Gen AMD EPYC CPUs and AMD Instinct MI300 Series accelerators.

MI300X

The AMD Instinct MI300X is based on the company’s CDNA 3 architecture. It packs 304 GPU cores. It also includes up to 192MB of HBM3 memory with a peak memory bandwidth of 5.3TB/sec. It’s available as 8 GPUs on an OAM baseboard.

The accelerator runs on the latest bus, the PCIe Gen 5, at 128GB/sec.

AI performance has been rated at 20.9 PFLOPS of total theoretical peak FP8 performance, AMD says. And HPC performance has a peak double-precision matrix (FP64) performance of 1.3 PFLOPS.

Compared with competing products, the AMD Instinct MI300X delivers nearly 40% more compute units, 1.5x more memory capacity, and 1.7x more peak theoretical memory bandwidth, AMD says.

AMD is also offering a full system it calls the AMD Instinct Platform. This packs 8 MI300X accelerators to offer up to 1.5TB of HBM3 memory capacity. And because it’s built on the industry-standard OCP design, the AMD Instinct Platform can be easily dropped into an existing servers.

The AMD Instinct MI300X is shipping now. So is a new Supermicro 8-GPU server with this new AMD accelerator.

MI300A

AMD describes its new Instinct MI300A as the world’s first data-center accelerated processing unit (APU) for HPC and AI. It combines 228 cores of AMD CDNA 3 GPU, 224 cores of AMD ‘Zen 4’ CPUs, and 128GB of HBM3 memory with a memory bandwidth of up to 5.3TB/sec.

AMD says the Instinct MI300A APU gives customers an easily programmable GPU platform, high-performing compute, fast AI training, and impressive energy efficiency.

The energy savings are said to come from the APU’s efficiency. As HPC and AI workloads are both data- and resource-intensive, a more efficient system means users can do the same or more work with less hardware.

The AMD Instinct MI300A is also shipping now. So are two new Supermicro servers that feature the APU, one air-cooled, and the other liquid-cooled.

ROCm 6

As part of its push into AI, AMD intends to maintain an open software platform. During CEO Su’s presentation, she said that openness is one of AMD’s three main priorities for AI, along with offering a broad portfolio and working with partners.

Victor Peng, AMD’s president, said the company has set as a goal the creation of a unified AI software stack. As part of that, the company is continuing to enhance ROCm, the company’s software stack for GPU programming. The latest version, ROCm 6, will ship later this month, Peng said.

AMD says ROCm 6 can increase AI acceleration performance by approximately 8x when running on AMD MI300 Series accelerators in Llama 2 text generation compared with previous-generation hardware and software.

ROCm 6 also adds support for several new key features for generative AI. These include FlashAttention, HIPGraph and vLLM.

AMD is also leveraging open-source AI software models, algorithms and frameworks such as Hugging Face, PyTorch and TensorFlow. The goal: simplify the deployment of AMD AI solutions and help customers unlock the true potential of generative AI.

Shipments of ROCm are set to begin later this month.

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Research Roundup: GenAI use, public-cloud spend, tech debt’s reach, employee cyber violations

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Research Roundup: GenAI use, public-cloud spend, tech debt’s reach, employee cyber violations

Catch up on the latest research from leading IT market watchers and analysts. 

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Generative AI is already used by two-thirds of organizations. Public-cloud spending levels are forecast to rise 20% next year. Technical debt is a challenge for nearly 75% of organizations. And info-security violations by staff are nearly as common as attacks by external hackers.

That’s some of the latest research from leading IT market watchers and analysts. And here’s your Performance Intensive Computing roundup.

GenAI already used by 2/3 of orgs

You already know that Generative AI is hot, but did you also realize that over two-thirds of organizations are already using it?

In a survey of over 2,800 tech professionals, publisher O’Reilly found that fully 67% of respondents say their organizations currently use GenAI. Of this group, about 1 in 3 also say their organizations have been working with AI for less than a year.

Respondents to the survey were users of O’Reilly products worldwide. About a third of respondents (34%) work in the software industry; 14% in financial services; 11% in hardware; and the rest in industries that include telecom, public sector/government, healthcare and education. By region, nearly three-quarters of respondents (74%) are based in either North America or Europe.

Other key findings from the O’Reilly survey (multiple replies were permitted):

  • GenAI’s top use cases: Programming (77%); data analysis (70%); customer-facing applications (65%)
  • GenAI’s top use constraints: Lack of appropriate use cases (53%); legal issues, risk and compliance (38%)
  • GenAI’s top risks: Unexpected outcomes (49%); security vulnerabilities (48%); safety and reliability (46%)

Public-cloud spending to rise 20% next year

Total worldwide spending by end users on the public cloud will rise 20% between this year and next, predicts Gartner. This year, the market watcher adds, user spending on the public cloud will total $563.6 billion. Next year, this spend will rise to $678.8 billion.

“Cloud has become essentially indispensable,” says Gartner analyst Sid Nag.

Gartner predicts that all segments of the public-cloud market will grow in 2024. But it also says 2 segments will grow especially fast next year: Infrastructure as a Service (IaaS), predicted to grow nearly 27%; and Platform as a Service (PaaS), forecast to grow nearly 22.

What’s driving all this growth? One factor: industry cloud platforms. These combine Software as a Service (SaaS), PaaS and IaaS into product offerings aimed at specific industries.

For example, enterprise software vendor SAP offers industry clouds for banking, manufacturing, HR and more. The company says its life-sciences cloud helped Boston Scientific, a manufacturer of medical devices, reduce inventory and order-management operational workloads by as much as 45%.

Gartner expects that by 2027, industry cloud platforms will be used by more than 70% of enterprises, up from just 15% of enterprises in 2022.

Technical debt: a big challenge

Technical debt—older hardware and software that no longer supports an organization’s strategies—is a bigger problem than you might think.

In a recent survey of 523 IT professionals, conducted for IT trade association CompTIA, nearly three-quarters of respondents (74%) said their organizations find tech debt to be a challenge.

An even higher percentage of respondents (78%) say their work is impeded by “cowboy IT,” shadow IT and other tech moves made without the IT department’s involvement. Not incidentally, these are among the main causes of technical debt, mainly because they are not acquired as part of the organization’s strategic goals.

Fortunately, IT pros are also fighting back. Over two-thirds of respondents (68%) said they’ve made erasing technical debt a moderate or high priority.

Cybersecurity: Staff violations nearly as widespread as hacks

Employee violations of organizations’ information-security policies are nearly as common as attacks by external hackers, finds a new survey by security vendor Kaspersky. 

The survey reached 1,260 IT and security professionals worldwide. It found that 26% of cyber incidents in business occurred due to employees intentionally violating their organizations’ security protocols. By contrast, hacker attacks accounted for 30%—not much higher.

Here’s the breakdown of those policy violations by employees, according to Kaspersky (multiple replies were permitted):

  • 25%: Using weak passwords or failing to change passwords regularly
  • 24%: Visiting unsecured websites
  • 24%: Using unauthorized systems for sharing data
  • 21%: Failing to update system software and applications
  • 21%: Accessing data with an unauthorized device
  • 20%: Sending data (such as email addresses) to personal systems
  • 20%: Intentionally engaging in malicious behavior for personal gain

The issue is far from theoretical. Among respondents to the Kaspersky survey, fully a third (33%) say they’ve suffered 2 or 3 cyber incidents in the last 2 years. And a quarter (25%) say that during the same time period, they’ve been the subject of at least 4 cyberattacks.

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Research Roundup: GenAI, 10 IT trends, cybersecurity, CEOs, and privacy

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Research Roundup: GenAI, 10 IT trends, cybersecurity, CEOs, and privacy

Catch up on the latest IT research and analysis from leading market watchers.

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Generative AI is booming. Ten trends will soon rock your customers’ world. While cybersecurity spending is up, CEOs lack cyber confidence. And Americans worry about their privacy.

That’s some of the latest from leading IT market watchers. And here’s your Performance Intensive Computing roundup.

GenAI market to hit $143B by 2027

Generative AI is quickly becoming a big business.

Market watcher IDC expects that spending on GenAI software, related hardware and services will this year reach nearly $16 billion worldwide.

Looking ahead, IDC predicts GenAI spending will reach $143 billion by 2027. That would represent a compound annual growth rate (CAGR) over the years 2023 to 2027 of 73%—more than twice the growth rate in overall AI spending.

“GenAI is more than a fleeting trend or mere hype,” says IDC group VP Ritu Jyoti.

Initially, IDC expects, the largest GenAI investments will go to infrastructure, including hardware, infrastructure as a service (IaaS), and system infrastructure software. Then, once the foundation has been laid, spending is expected to shift to AI services.

Top 10 IT trends

What will be top-of-mind for your customers next year and beyond? Researchers at Gartner recently made 10 predictions:

1. AI productivity will be a primary economic indicator of national power.

2. Generative AI tools will reduce modernization costs by 70%.

3. Enterprises will collectively spend over $30 billion fighting “malinformation.”

4. Nearly half of all CISOs will expand their responsibilities beyond cybersecurity, driven by regulatory pressure and expanding attack surfaces.

5. Unionization among knowledge workers will increase by 1,000%, motivated by fears of job loss due to the adoption of GenAI.

6. About one in three workers will leverage “digital charisma” to advance their careers.

7. One in four large corporations will actively recruit neurodivergent talent—including people with conditions such as autism and ADHD—to improve business performance.

8. Nearly a third of large companies will create dedicated business units or sales channels for machine customers.

9. Due to labor shortages, robots will soon outnumber human workers in three industries: manufacturing, retail and logistics.

10. Monthly electricity rationing will affect fully half the G20 nations. One result: Energy efficiency will become a serious competitive advantage.

Cybersecurity spending in Q2 rose nearly 12%

Heightened threat levels are leading to heightened cybersecurity spending.

In the second quarter of this year, global spending on cybersecurity products and services rose 11.6% year-on-year, reaching a total of $19 billion worldwide, according to Canalys.

A mere 12 vendors received nearly half that spending, Canalys says. They include Palo Alto Networks, Fortinet, Cisco and Microsoft.

One factor driving the spending is fear, the result of a 50% increase in the number of publicly reported ransomware attacks. Also, the number of breached data records more than doubled in the first 8 months of this year, Canalys says.

All this increased spending should be good for channel sellers. Canalys finds that nearly 92% of all cybersecurity spending worldwide goes through the IT channel.

CEOs lack cyber confidence

Here’s another reason why cybersecurity spending should be rising: Roughly three-quarters of CEOs (74%) say they’re concerned about their organizations’ ability to avert or minimize damage from a cyberattack.

That’s according to a new survey, conducted by Accenture, of 1,000 CEOs from large organizations worldwide.

Two findings from the Accenture survey really stand out:

  • Nearly two-thirds of CEOs (60%) say their organizations do not incorporate cybersecurity into their business strategies, products or services
  • Nearly half (44%) the CEOs believe cybersecurity can be handled with episodic interventions rather than with ongoing, continuous attention.

Despite those weaknesses, nearly all the surveyed CEOs (96%) say they believe cybersecurity is critical to their organizations’ growth and stability. Mind the gap!

How do Americans view data privacy?

Fully eight in 10 Americans (81%) are concerned about how companies use their personal data. And seven in 10 (71%) are concerned about how their personal data is used by the government.

So finds a new Pew Research Center survey of 5,100 U.S. adults. The study, conducted in May and published this month, sought to discover how Americans think about privacy and personal data.

Pew also found that Americans don’t understand how their personal data is used. In the survey, nearly eight in 10 respondents (77%) said they have little to no understanding of how the government uses their personal data. And two-thirds (67%) said the same thing about businesses, up from 59% a year ago.

Another key finding: Americans don’t trust social media CEOs. Over three-quarters of Pew’s respondents (77%) say they have very little or no trust that leaders of social-medica companies will publicly admit mistakes and take responsibility.

And about the same number (76%) believe social-media companies would sell their personal data without their consent.

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Research Roundup: Edge, channel sales, insider risk, AI security, wireless LANs

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Research Roundup: Edge, channel sales, insider risk, AI security, wireless LANs

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Edge computing is strategic. The IT channel is huge. Insider cyber risks deserve more attention. AI can be used to oversee AI. And enterprises are buying more wireless LANs.

That’s the latest from top IT market research. And here’s your Performance Intensive Computing roundup.

All hail the edge

More than 8 in 10 C-level executives (83%) believe that to remain competitive in the future, their organizations will need to implement edge computing.

Nearly as many (81%) believe that if they fail to act quickly on edge computing, they could be locked out from enjoying the technology’s full benefits.

Those figures come from a new study by Accenture. The report is based on a poll, conducted by the consulting firm late last year, of 2,100 C-suite execs—including 250 CEOs—across 18 industries and 16 countries.

There’s plenty of room for progress on the edge, the Accenture poll finds. Just two-thirds (65%) of companies use edge today. And among these adopters, only half have integrated edge into their digital core.

Edge systems can be enhanced with the cloud. Indeed, Accenture finds that nearly 8 in 10 respondents (79%) say they’ll fully integrate edge with cloud in the next three years.

Channel rules

How important is the IT channel? Very, according to market watcher Canalys.

Canalys expects that this year, partner-delivered IT technologies and services worldwide will total more than $3.4 trillion, or about 70% of the global addressable IT market.

And the market is rising, despite ongoing economic issues. Canalys predicts the worldwide IT market will rise 3.5% this year, for a full-year total of $4.7 trillion.

Some of the biggest growth opportunities this year coming in cybersecurity (with sales forecast to rise 11%), network infrastructure (14%) and public cloud (7.5%), according to Canalys.

There are also big implications for the IT hardware, software and services suppliers that rely on the channel.

“Given the importance of the channel,” says Canalys chief analyst Matthew Ball, “the success of vendors will increasingly rely on their resell, co-sell, co-marketing, co-retention, co-development and co-innovation strategies.”

Insider risk rising

Here’s a new reason to worry: The average annual cost of an insider cyber risk has risen 40% over the last 4 years, reaching $16.2 million. And the average amount of time it takes to contain an insider incident is now a about 3 months (86 days).

That’s according to a new study conducted by the Ponemon Institute on behalf of Dtex Systems, a supplier of risk-management software. Their new joint report is based on a recent survey of 1,075 security and line-of-business professionals at nearly 310 organizations worldwide.

Despite this risk, the survey finds that most organizations are dedicating only about 8% of their overall cybersec budget—the equivalent of $200 per employee—to insider threats.

What’s more, about 90% of the insider-risk budget gets spent after an insider incident has occurred, the survey found. These after-incident costs include containment, remediation, investigation, incident response and escalation.

AI vs. AI?

AI-powered risks may be so stealthy, only another AI system can fight them off.

That’s the sentiment revealed by a new Gartner survey. The research firm finds that about 1 in 3 organizations (34%) now use AI application security tools to mitigate the risks of generative AI. Over half (56%) are exploring such approaches for the future.

These numbers come from Gartner’s most recent Peer Community survey, conducted in April. Gartner collected responses from 150 IT and cybersecurity leaders at organizations that use either GenAI or foundational models.

When asked which risks of GenAI worry them the most, nearly 6 in 10 respondents (57%) said leaked secrets in AI-generated code. About the same number (58%) said they’re concerned about AI generating incorrect or biased outputs.

“Organizations that don’t manage AI risk will witness their models not performing as intended,” says Gartner analyst Avivah Litan. “In the worst case [AI] can cause human or property damage.”

Enterprise wireless LAN heats up

Looking for a new growth market? Consider the enterprise segment of wireless local area networking. In this year’s second quarter, sales in this sector grew 43%, reaching a total of $3 billion, according to market intelligence firm IDC.

The growth rate was even higher in both the United States and Canada. In both countries, Q2 sales of wireless LANs to enterprises rose nearly 80% year-on-year, IDC says.

By contrast, the consumer end of the wireless LAN market declined by 14% year-on-year in Q2, according to IDC.

Driving the enterprise sales are a couple of factors, including an easing of both components shortages and supply-chain disruptions, says IDC researcher Brandon Butler. Another growth factor is the rapid adoption by enterprises of the new Wi-Fi 6 and 6E standards.

 

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Tech Explainer: What is the intelligent edge? Part 1

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Tech Explainer: What is the intelligent edge? Part 1

The intelligent edge moves compute, storage and networking capabilities close to end devices, where the data is being generated. Organizations gain the ability to process and act on that data in real time, and without having to first transfer that data to the a centralized data center.

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The term intelligent edge refers to remote server infrastructures that can collect, process and act on data autonomously. In effect, it’s a small, remote data center.

Compared with a more traditional data center, the intelligent edge offers one big advantage: It locates compute, storage and networking capabilities close to the organization’s data collection endpoints. This architecture speeds data transactions. It also makes them more secure.

The approach is not entirely new. Deploying an edge infrastructure has long been an effective way to gather data in remote locations. What’s new with an intelligent edge is that you gain the ability to process and act on that data (if necessary) in real time—without having to first transfer that data to the cloud.

The intelligent edge can also save an organization money. Leveraging the intelligent edge makes sense for organizations that spend a decent chunk of their operating budget transferring data from the edge to public and private data centers, which could be a cloud infrastructure (often referred to as “the core”). Reducing bandwidth in both directions and storage charges helps them control costs.

3 steps to the edge

Today, an intelligent edge typically gets applied in one of three areas:

  • Operational Technology (OT): Hardware and software used to monitor and control industrial equipment, processes and events.
  • Information Technology (IT): Digital infrastructure—including servers, storage, networking and other devices—used to create, process, store, secure and transfer data.
  • Internet of Things (IoT): A network of smart devices that communicate and can be controlled via the internet. Examples include smart speakers, wearables, autonomous vehicles and smart-city infrastructure.

The highly efficient edge

There’s yet another benefit to deploying intelligent edge tech: It can help an organization become more efficient.

One way the intelligent edge does this is by obviating the need to transfer large amounts of data. Instead, data is stored and processed close to where it’s collected.

For example, a smart lightbulb or fridge can communicate with the intelligent edge instead of contacting a data center. Staying in constant contact with the core is unnecessary for devices that don’t change much from minute to minute.

Another way the intelligent edge boosts efficiency is by reducing the time needed to analyze and act on vital information. This, in turn, can lead to enhanced business intelligence that informs and empowers stakeholders. It all gets done faster and more efficiently than with traditional IT architectures and operations.

For instance, imagine that an organization serves a large customer base from several locations. By deploying an intelligent edge infrastructure, the organization could collect and analyze customer data in real time.

Businesses that gain insights from the edge instead of from the core can also respond quickly to market changes. For example, an energy company could analyze power consumption and weather conditions at the edge (down to the neighborhood), then determine whether there's be a power outage.

Similarly, a retailer could use the intelligent edge to support inventory management and analyze customers’ shopping habits. Using that data, the retailer could then offer customized promotions to particular customers, or groups of customers, all in real time.

The intelligent edge can also be used to enhance public infrastructure. For instance, smart cities can gather data that helps inform lighting, public safety, maintenance and other vital services, which could then be used for preventive maintenance or the allocation of city resources and services as needed.

Edge intelligence

As artificial intelligence (AI) becomes increasingly ubiquitous, many organizations are deploying machine learning (ML) models at the edge to help analyze data and deliver insights in real time.

In one use case, running AI and ML systems at the edge can help an organization reduce the service interruptions that often come with transferring large data sets to and from the cloud. Intelligent Edge is able to keep things running locally, giving distant data centers a chance to catch up. This, in turn, can help the organization provide a better experience for the employees and customers who rely on that data.

Deploying AI at the edge can also help with privacy, security and compliance issues. Transferring data to and from the core presents an opportunity for hackers to intercept data in transit. Eliminating this data transfer deprives cyber criminals of a threat vector they could otherwise exploit.

Part 2 of this two-part blog series dives deep into the biggest, most popular use of the intelligent edge today—namely, the internet of things (IoT). We also look at the technology that powers the intelligent edge, as well as what the future may hold for this emerging technology.

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Research Roundup: AI chip sales, AI data centers, sustainability services, manufacturing clouds, tech-savvy or not

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Research Roundup: AI chip sales, AI data centers, sustainability services, manufacturing clouds, tech-savvy or not

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Sales of AI semiconductors are poised for big growth. AI is transforming the data center. Sustainability services are hot. Manufacturers are saving big money with cloud. And Americans are surprisingly lacking in tech understanding.

That’s some of the latest IT market research. And here’s your Performance Intensive Computing roundup.

AI chip sales to rise 21% this year

Sales of semiconductors designed to execute AI workloads will rise this calendar year by 20.9% over last year, reaching a worldwide total of $53.4 billion, predicts research firm Gartner.

Looking further ahead, Gartner expects worldwide sales of AI chips in 2024 to reach $67.1 billion, a 25% increase over the projected figure for this year.

And by 2027, Gartner forecasts, those sales will top $119 billion, or more than double this year’s market size.

What’s behind the rapid rise? Two main factors, says Gartner: Generative AI, and the spread of AI-based applications in data centers, edge infrastructure and endpoint devices.

AI transforming data centers

Generative AI is transforming the data center, says Lucas Beran, a market analyst with Dell’Oro Group. Last month, his research group predicted that AI infrastructure spending will propel the data center CapEx to over a half-trillion dollars by 2027, an increase of 15%. (That figure is larger than Gartner’s because it includes more than just chips.) Now Dell’Oro says AI is ushering in a new era for data center physical infrastructure.

Here’s some of what Beran of Dell’Oro expects:

  • Due the substantial power consumption of AI systems, end users will adopt intelligent rack power distribution units (PDUs) that can remotely monitor and manage power consumption and environmental factors.
  • Liquid cooling will come into its own. Some users will retrofit existing cooling systems with closed-loop assisted liquid cooling systems. These use liquid to capture heat generated inside the rack or server, then blow it into a hot aisle. By 2025, global sales of liquid cooling systems will approach $2 billion.
  • A lack of power availability could slow AI adoption. Data centers need more energy than utilities can supply. One possible solution: BYOP – bring your own power.

Sustainability services: $65B by 2027

Speaking of power and liquid cooling, a new forecast from market researcher IDC has total sales of environmental, social and governance (ESG) services rising from $37.7 billion this year to nearly $65 billion by 2027, for a compound annual growth rate (CAGR) of nearly 15%.

For its forecast, IDC looked at ESG services that include consulting, implementation, engineering and IT services.

These services include ESG strategy development and implementation, sustainable operations consulting, reporting services, circularity consulting, green IT implementation services, and managed sustainability performance services. What they all share is the common goal of driving sustainability-related outcomes.

Last year, nearly two-thirds of respondents surveyed by IDC said they plan to allocate more than half their professional-services spending on sustainability services. Looking ahead, IDC expects that to rise to 60% by 2027.

"Pressure for [ESG] change is more prescient than ever,” says IDC research analyst Dan Versace. “Businesses that fail to act face risk to their brand image, financial performance, and even their infrastructure due to the ever-present threat of extreme weather events and resource shortages caused by climate change.”

Manufacturers finally see the cloud

For manufacturers, IT is especially complicated. Unlike banks and other purely digital businesses, manufacturers have to tie IT systems and networks with physical plants and supply chains.

That’s one reason why manufacturers have been comparatively slow to adopt cloud computing. Now that’s changing. In part, as a new report from ABI Research points out, because manufacturers that switch to cloud-based systems can enjoy up to 60% reductions in overhead costs relating to data storage, says James Iversen, an ABI industry analyst.

Iversen predicts that industrial cloud platform revenue in manufacturing will enjoy a nearly 23% CAGR for the coming decade.

Another benefit for manufacturers: The cloud can eliminate the data fragmentation common with external data warehouses. “Cloud manufacturing providers are eliminating these concerns by interconnecting applications bi-directionally,” Iversen says, “leading to sharing and communication between applications and their data.”

How tech-savvy are your customers?

If they’re like most Americans, not very.

A Pew Research Center poll of about 5,100 U.S. adults, conducted this past spring and just made public, found that fewer than a third (32%) knew that large language models such as ChatGPT produce answers from data already published on the internet.

Similarly, only about one in five (21%) knew that U.S. websites are prohibited from collecting data on minors under the age of 13.

Fewer than half of those polled (42%) knew what a deepfake is. And only a similar minority (48%) could identify an example of two-factor authentication.

What tech info do they know? Well, 80% of respondents correctly identified Elon Musk as the boss of Tesla and Twitter (now X). And nearly as many (77%) knew that Facebook had changed its name to Meta.

 

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