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Mundo Chip tenta se aproximar da promessa e do perigo da IA


Os executivos da indústria de Chip reuniram-se em São Francisco para discutir o que fazer com a explosão da demanda por formas de aprendizagem profunda da IA ​​que está empurrando os limites dos chips de hoje.

Applied Materials chief executive Gary Dickerson.

As possibilidades incluem computação analógica, circuitos ópticos e computação na memória, mas não está claro se isso irá desviar de um potencial gargalo de computação em escala de armazenamento.

A indústria de computadores enfrenta mudanças épicas, já que as demandas das formas de aprendizagem profunda e impõem novas exigências ao vale do silício, ao mesmo tempo em que a Lei de Moore, a regra de décadas do progresso no setor de chips, está entrando em colapso.

Esta semana, algumas das melhores mentes da indústria de chips se reuniram em São Francisco para falar sobre o que isso significa.

Leia a matéria em inglês.

Gary Dickerson, chief executive of Applied Materials, started his talk by noting the “dramatic slowdown of Moore’s Law”, citing data from UC Berkeley Professor David Patterson and Alphabet chairman John Hennessy showing that new processors are improving in performance by only 3.5% per year.

(The figure is slightly outdated; an essay by Patterson and Hennessy back in February pegged the slowdown to 3% improvement per year.)

Dickerson went on to claim that A.I. workloads in data centers worldwide could come to represent as much as 80% of all compute cycles and 10% of global electricity use in the next decade or so.

That means the industry needs to seek many routes to solutions, said Dickerson, including “new architectures” for chip design and new kinds of memory chips.

He cited several types of memory, including “MRAM”, “ReRAM”, (resistive RAM), “PCRAM”, (phase-change RAM), and “FeRAM”.

The industry would also have to explore analog chip designs, chips that manipulate data as continuous, real-valued signals, rather than discrete units, and new kinds of materials beyond silicon.

Both Advanced Micro Devices’s chief, Lisa Su, and Xilinx’s CEO, Victor Peng, made a pitch for their respective roles in making possible heterogenous types of computing.

Su talked about the company’s “Epyc”, server chip, which is working around the Moore’s Law bottleneck by gathering together multiple silicon dice, called “chiplets”, into a single package, with a high-speed memory bus connecting the chiplets, to build a kind of chip that is its own computer system.

Lots of new memory types are among the measures industry will need to focus on to contend with the sharp rise in A.I. workloads.Applied Materials

Peng rehashed remarks from the company’s May investor day in New York, saying that Xilinx’s programmable chips, “FPGAs”, can handle not only the matrix multiplications of A.I. but also the parts of traditional software execution that need to happen before and after the machine learning operations.

A senior Google engineer, Cliff Young, went into the details of the Tensor Processing Unit, or “TPU” chip that Google developed starting in 2013. The effort was prompted, he said, by a kind of panic.

The company saw that with more and more machine learning services running at Google, “matrix multiplications were becoming a noticeable fraction of fleet cycles”, in Google data centers.

“What if everyone talks to their phones two minutes a day, or wants to analyze video clips for two minutes a day”, using machine learning, he asked rhetorically. “We don’t have enough computers”.

“There was potential in that for both success and disaster”, he said of the exploding demand for A.I. services. “We began a 15-month crash project to achieve a ten-X improvement in performance”.

Despite now being on the third iteration of the TPU, Young implied the crisis is not over. Compute demand is increasing “cubicly”, he said, speaking of matrix multiplications.

Google has whole warehouse-sized buildings full of “pods”, containers that have multiple racks filled with TPUs. Still it won’t be enough. “Even Google will reach limits to how we can scale data centers”.

Get ready for a warehouse bottleneck, in other words.

Google engineer, Cliff YoungKelsey Floyd

Young said there will have to be a lot of collaboration between hardware designers and software programmers, what he called “co-design”, but also co-design with materials physicists, he suggested.

“When you do co-design, it’s interdisciplinary work, and you are a stranger in a strange land”, he observed. “We have to get out of our comfort zone”.

“Can we use optical transceivers” to manipulate neural nets, he wondered. Optical computing is “awesome at matrix multiplication”, he observed, but it is not very good at another critical part of neural networks, the nonlinear activation functions of each artificial neuron.

“Packaging is a thing, what more can we do with packaging and chiplets?” he asked. The industry needs alternatives to CMOS, the basic silicon material of chips, he said, echoing Dickerson.

In-memory computing will also be important, he said, having computations close to memory cells rather than moving back and forth, to and from memory to processor and back along a conventional memory bus.

Young offered that machine learning might open new opportunities for analog computing. “It’s weird that we have this digital layer between the real-numbered neural nets and the underlying analog devices”, he said, drawing a connection between the statistical or stochastic nature of both A.I. and silicon. “Maybe we don’t always need to go back into bits all the time”, mused Young.

Given all the challenges, it’s a super-cool time to be guiding.

Young was followed by the head of process technology at wireless chip giant Qualcomm, PR “Chidi” Chidambaram. Qualcomm has said it will make chips this year to do A.I. computing in the cloud, but Chidambaram’s focus was the “inference” stage of machine learning, making predictions, and explicitly in “edge” devices such as the mobile phone.

He, like Dickerson, emphasized the importance of memory, and said that what he referred to as “CIM” or compute in memory, “is going to do computation very close to where the data is”, and that it will constitute a “paradigm shift in compute”.

At the end of the day was a panel discussion with five venture capitalists on the topic of how to fund new companies in cutting-edge areas such as A.I. The moderator was none other than the author of this article.

The panelists included Shahin Farshchi, managing partner with Lux Capital; Laura Oliphant, general partner with Spirit Ventures; Aymerik Reynard, general partner with Hardware Club; Rajesh Swaminathan, the general manager of Applied Ventures, the venture capital arm of Applied Materials; and Jennifer Ard, an investment director with Intel’s venture arm, Intel Capital.

To open the session, I asked each of the panelists whether Moore’s Law is dead, yes or no. Although each panelist hemmed and hawed a bit, when pressed, four of the five said that “yes”, Moore’s Law is dead.

Farshchi, who answered last, said “no”. His explanation was that while Moore’s Law may no longer predict semiconductor progress in terms of the physics of transistor improvement, the same growth in compute performance is ultimately able to be had from the entire computing ecosystem at large.

In a sense, that’s in line with much of the rest of the day’s talk, whether or not it’s literally accurate. It’s going to take an entire  industry to adjust meet the demands of A.I.

Fonte: ZDNet

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