BRIAN SANTO: I’m Brian Santo, EE Times Editor in Chief, and you're listening to EETimes on Air. This is your Briefing for the week ending June 28th.
我是EE Times主编Brian Santo，你正在收听的是EETimes全球联播。这是截至6月28日的一周新闻概要。
This week…
本周……
The French research institute, LETI, held a conference on artificial intelligence at the edge. What does putting AI on the edge of the network mean, and what’s the advantage? EETimes editors were in Grenoble, and filed the report.
法国研究机构LETI主办了一场有关边缘AI的会议。将AI放在网络边缘是什么意思，有什么优势？EETimes编辑从法国Grenoble对此进行了报道。
A few weeks ago, PCI introduced a new ultra-fast networking specification that will make data centers perform even better, and that will make the internet faster and more capable. A few days ago, PCI unexpectedly doubled the speed again with yet another spec.
几周前，PCI发布了新的超快速网络规范，将使数据中心的性能更加强劲，让互联网更快、更强。就在几天前，PCI又出乎意料地提出另一个规范，将速度再次提高了一倍。
EE Times editors were at the annual Sensors Expo, which has become an important conference for the Internet of Things. We’ll have a report.
EE Times编辑参加了今年的传感器博览会，这已成为物联网的一个重要展会。我们也会有专门报道。
Also, the prevailing wisdom is that self-driving vehicles will be safer than human drivers. But ,what if there’s a third option – one that’s just as safe as self-driving cars are supposed to be?
此外，普遍认为自动驾驶车辆将比人类驾驶员更安全。但是，如果有第三种选择--一种与自动驾驶汽车一样安全的选择会怎样？
…We’ll hear about that in a moment.
......我们马上就会听到这个消息。
Data centers and other computing clusters simply can’t go fast enough. In the last few weeks, the PCI special interest group introduced not one but two generations of the PCI spec. PCI will accelerate the data centers that are now central to the modern internet, but adopting the new specs will force some significant changes in how data centers get configured.
数据中心和其他计算集群的速度总是不够快。在过去几周里，PCI特别兴趣组发布了两个(不是一个)PCI规范。数据中心已成为现代互联网的核心，PCI规范无疑将加速数据中心的性能。但要采用新的规范，数据中心的配置方式必须做出重大改变。
Here’s Rick Merritt on the new spec and what it means.
有请Rick Merritt为我们介绍这一新规范及其含义。
以下是英文原稿，Enjoy!
RICK MERRITT: The PCI special interest group surprised many of us at their annual event last week when they announced plans for Gen 6, a 64 gigatransfer per second version of PCI Express. They had just finished last month their Gen 5 at 32 gigatransfers per second, so doubling the interconnect speed within two years is really pretty impressive.
Now, they're really just leveraging the Pam 4 technology with Ford error correction that's been sorted out and developed for high end CERTIs; but nevertheless, to implement that in a kind of a mainstream specification that has to go into high volume servers and even PCs is going to be quite a challenge. And I'm sure it's going to take them the whole two years.
One of the interesting aspects here is that we understand from talking to people on the show floor there that copper really still has a long life ahead of it. There's three specs in the optical interconnect forum: there's an IEEE backplane spec, uh, that are already running 112 gig interconnect speeds on copper. And I'm told by one startup that they say that they certainly see a way forward to doubling that to 224 gigs in the future. And they even have ideas to doubling that to 500 or so gigs. So, a lot of room for copper interconnects.
The trouble is, the faster they go, the shorter they go. And it's just the laws of physics, folks. So the system designers are already starting to look at ways that they can do smaller boards inside the servers and networking gear, and then do cable links between them. It's a little more expensive, but it's a lot less expensive than moving from FR4 to higher board materials or using retimer chips.
So faster interconnects are coming, but they're going to force a redesign of board-level products. And that's a pretty interesting and pretty broad impact.
This is Rick Merritt reporting from Santa Clara, California for EETimes on Air.
BRIAN SANTO: CEA Leti is French research institute that specializes in microelectronics and nanotechnology. The institute hosts an annual conference called “Innovation Days.” The theme this year was “Deep Tech for Edge AI.”
Where you physically host AI resources in the network makes a difference. Emmanuel Sabonnadière is the chief executive of CEA Leti. We asked him to explain the two basic approaches for architecting AI in the network.
EMMANUEL SABONNADIÈRE: To develop an AI model, you need to have a lot of data and a lot of power of computations. For that, you develop the model in the Cloud. After that, you have to use the model for the real life. You can either keep it in the Cloud or you can have it more local. That means having Edge AI.
So what I can see today is that the Chinese or the Americans are more thinking having the model running into the Cloud, because they have easy access to the data, so real-life data. Where in Europe, due to the data privacy, you need to have it more local, more personal. And for that…the more the one in Europe where we try to develop chip that will support Edge AI as a technology.
BRIAN SANTO: EE Times editors Nitin Dahad and Junko Yoshida were both at CEA Leti in Grenoble. They filed this report.
NITIN DAHAD: So Leti was actually...We've been talking about embedded intelligence for a while.
JUNKO YOSHIDA: Yeah.
NITIN DAHAD: And I think what Leti was looking to demonstrate is the real sort of deep tech behind it for AJI. And it comes from a lot of what Emmanuel, the CEO of Leti, said earlier, which was basically addressing some of the issues around data privacy in Europe.
JUNKO YOSHIDA: Right. That's right. Yeah, he actually made it very clear that, unlike China/US model, AI model, Europe... you know, their hands are tied because of the strict regulations of privacy, data privacy, they would have to solve this problem of Edge. It's a hard problem to solve. And they think they see their mission, Europe's mission, in AI is the Edge AI. But you know, listen, you and talked about, Well, Edge AI is almost like an over-hyped, overused in our coverage.
So tell me the building blocks of Edge AI CEA Leti is talking about here.
NITIN DAHAD: So actually that's quite important, because they're talking really about the nonvolatile memory in memory computing, spiking neural networks, the process of technology, FDSOI. And I think what they're trying to demonstrate is, you can't really have the Edge AI without the hardware. And that's actually a very important building block. So without that... that's what the research going on in, for example, the spirit chip we saw today, the project that Leti has been working on with the analog synapse, is quite an interesting application. And then we also know... We're looking at, for example, the stacked memory and processing, you know, the thing that came out of Stanford that we wrote about last year.
And I think just how putting all of that architecture together to basically reduce the data bottleneck. And I think that's key.
JUNKO YOSHIDA: Yeah. I think they talked about not just bottleneck, I think one of the researchers said, Don't move data! Because every time when you move data from memory blocks, external memory blocks, to the processor, it use a lot of energy. So right? So there's energy efficiency is one. There is another thing that they talk about: reduce the number of operations. And that's where I think spiking neural networks and event-driven stuff that comes in. So these are the two things.
One of the things I wanted to ask you, what was the most interesting application of Edge AI that we heard today?
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