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takyon writes:

Intel's CEO Bob Swan is looking beyond CPU market share:

"We think about having 30% share in a $230 billion [silicon] TAM[*] that we think is going to grow to $300 billion [silicon] TAM over the next 4 years, and frankly, I'm trying to destroy the thinking about having 90% share inside our company because, I think it limits our thinking, I think we miss technology transitions. we miss opportunities because we're, in some ways pre-occupied with protecting 90, instead of seeing a much bigger market with much more innovation going on, both Inside our four walls, and outside our four walls, so we come to work in the morning with a 30% share, with every expectation over the next several years, that we will play a larger and larger role in our customers success, and that doesn't just [mean] CPUs.

It means GPUs, it means Al, it does mean FPGAs, it means bringing these -technologies together so we're solving customers' problems. So, we're looking at a company with roughly 30% share in a $288 billion silicon TAM, not CPU TAM but silicon TAM. We look at the investments we've been making over the last several years in these kind of key technology inflections: 5G At autonomous, acquisitions, including Altera, that we think is more and more relevant both in the cloud but also ai the network and at the edge, and we see a much bigger opportunity, and our expectations are that we're going to gain our fair share at that much larger TAM by Investing in these key technology inflections." - Intel CEO Bob Swan

A 30% TAM in all of silicon would mean that Intel not only has more room to grow but is a lot more diversified as well. With the company working on the Nervana processor as well as its Xe GPU efforts, it seems poised to start clawing market share in new markets. Interestingly, it also means that Intel is not interested in defending its older title of being the CPU champion and will actually cede space to AMD where required. To me, this move is reminiscent of Lisa Su's decision to cede space in the GPU side of things to turn AMD around.

Intel's business strategy is now focused on whatever an "XPU" is as well as GPUs, FPGAs, machine learning accelerators, and next-generation memory/storage:

This means the company intends to continue making its heaviest bets in areas such as Optane storage, hardware Artificial Intelligence acceleration, 5G modems, data center networking, and more. The slide that really drives this commitment home comes from Q2's investor meeting that explicitly shows the company moving from a "protect and defend" strategy to a growth strategy. If this slide were in a sales meeting, it wouldn't say much—but delivered to the company's investors, it gains a bit of gravitas.

Most of this was revealed nearly six months ago at the company's May 2019 investor's meeting, but the Q3 investor's meeting last week continues with and strengthens this story for Intel's future growth, with slides more focused on Optane, network, and IoT/Edge market growth than with the traditional PC and server market.

[*] TAM = Total Addressable Market.

Related: Intel Promises "10nm" Chips by the End of 2019, and More
Intel's Interim CEO Robert Swan Becomes Full-Time CEO
AMD Gains Market Share in Desktops, Laptops, and Servers as of Q4 2018
PC Market Decline Blamed on Intel, AMD to See Gains
Intel Chip Shortages - at Least Another Quarter or Two to Go, Say PC Execs
Intel announces $20 billion increase in stock buybacks (from $4.5 billion)
Intel Xe High Performance Computing GPUs will use Chiplets

Original Submission

takyon writes:

Write AI code once, run anywhere—it's not Java, it's Intel's oneAPI

Saturday afternoon (Nov. 16) at Supercomputing 2019, Intel launched a new programming model called oneAPI. Intel describes the necessity of tightly coupling middleware and frameworks directly to specific hardware as one of the largest pain points of AI/Machine Learning development. The oneAPI model is intended to abstract that tight coupling away, allowing developers to focus on their actual project and re-use the same code when the underlying hardware changes.

This sort of "write once, run anywhere" mantra is reminiscent of Sun's early pitches for the Java language. However, Bill Savage, general manager of compute performance for Intel, told Ars that's not an accurate characterization. Although each approach addresses the same basic problem—tight coupling to machine hardware making developers' lives more difficult and getting in the way of code re-use—the approaches are very different.

[...] When we questioned Savage about oneAPI's design and performance expectations, he distanced it firmly from Java, pointing out that there is no bytecode involved. Instead, oneAPI is a set of libraries that tie hardware-agnostic API calls directly to heavily optimized, low-level code that drives the actual hardware available in the local environment. So instead of "Java for Artificial Intelligence," the high-level takeaway is more along the lines of "OpenGL/DirectX for Artificial Intelligence."

For even higher-performance coding inside tight loops, oneAPI also introduces a new language variant called "Data Parallel C++" allowing even very low-level optimized code to target multiple architectures. Data Parallel C++ leverages and extends SYCL, a "single source" abstraction layer for OpenCL programming.

In its current version, a oneAPI developer still needs to target the basic hardware type he or she is coding for—for example, CPUs, GPUs, or FPGAs. Beyond that basic targeting, oneAPI keeps the code optimized for any supported hardware variant. This would, for example, allow users of a oneAPI-developed project to run the same code on either Nvidia's Tesla v100 or Intel's own newly released Ponte Vecchio GPU.

Related: Intel Xe High Performance Computing GPUs will use Chiplets

Original Submission