新光子芯片突破：速度提高 1000 倍，是真的嗎？
新光子芯片突破：速度提高 1000 倍。是真的嗎？
譯文簡(jiǎn)介
網(wǎng)友：這絕對(duì)是很受歡迎的，光子學(xué)需要更多的新聞報(bào)道。這篇論文的分析得真棒!
正文翻譯
新光子芯片突破：速度提高 1000 倍。是真的嗎？
Anastasi In Tech
2024年3月16日
原創(chuàng)翻譯：龍騰網(wǎng) http://nxnpts.cn 轉(zhuǎn)載請(qǐng)注明出處
2024年3月16日
原創(chuàng)翻譯：龍騰網(wǎng) http://nxnpts.cn 轉(zhuǎn)載請(qǐng)注明出處
Timestamps:
00:00 - Intro
03:16 - Lithium Niobate
05:56 - How does this chip work?
時(shí)間戳:
00:00 -引言
03:16 -鈮酸鋰
05:56 -這種芯片是怎么工作的?
00:00 - Intro
03:16 - Lithium Niobate
05:56 - How does this chip work?
時(shí)間戳:
00:00 -引言
03:16 -鈮酸鋰
05:56 -這種芯片是怎么工作的?
評(píng)論翻譯
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This is absolutely HUGE. Photonics needs way more coverage. Great breakdown of the paper!
這絕對(duì)是很受歡迎的，光子學(xué)需要更多的新聞報(bào)道。這篇論文的分析得真棒!
This lady can easily be a professor in practically 99 percent of the Universities of the world! In one compelling video she bring so much "light" to the basic understanding of the world of photonic chips.
As Einstein once said to the effect, if you cannot explain something simply so that a six year old can also understand it, then you do not know it yourself!
這位女士可以輕而易舉地成為世界上99%的大學(xué)的教授!在一個(gè)引人注目的視頻中，她為光子芯片世界的基本理解帶來了很多“光”
正如愛因斯坦曾經(jīng)說過的那樣，如果你不能簡(jiǎn)單地解釋一件事，以至于一個(gè)六歲的孩子也能理解它，那么你自己也沒有了解它!
I remember writing a paper on optical computing back in the late 80's. There were high hopes back then. Not much has happened in this space since that time. It's encouraging to see some progress.
我記得在80年代末寫過一篇關(guān)于光學(xué)計(jì)算的論文。當(dāng)時(shí)人們寄予厚望，從那時(shí)起，這個(gè)領(lǐng)域都沒有什么太過有影響力的事情，看到一些進(jìn)展令人鼓舞。
I ran an optical switching startup in the late 90's. Met several times with the NSA to explore optical computing. Nice to see it developing.
我在90年代末開了一家光交換公司。曾幾次與國(guó)家安全局會(huì)面探討光學(xué)計(jì)算。很高興看到它的發(fā)展。
It is funny. Your comment bought to mind Stephen Wolfram's idea of "computing" (where pretty much anything that exists dynamically is doing that). Actually, IMO, there have been huge advances in photovoltaic and photonics in the last 20-30 years. Your high resolution TV and display screens are examples, as are solar power and Starlix and other satellite networks interconnected by lasers, not to mention bio-medical applications, GPS, all sorts of sensors, etc. Google says China is first in this field - but that may be for applications. Numerous universities (Colorado at Boulder, Stanford, MIT, University of Rochester come to mind) are doing most of the world-class research in these fields IMO.
這很有趣。你的評(píng)論讓人想起了史蒂芬·沃爾夫勒姆（Stephen Wolfram）關(guān)于“計(jì)算”的想法（幾乎任何動(dòng)態(tài)存在的東西都是這樣做的）。事實(shí)上，在我看來，在過去的20-30年里，光伏和光子學(xué)取得了巨大的進(jìn)步。你用的高分辨率電視和顯示屏就是例子，太陽能、星鏈和其他通過激光互連的衛(wèi)星網(wǎng)絡(luò)也是例子，更不用說生物醫(yī)療應(yīng)用、GPS、各種傳感器了。谷歌表示，中國(guó)在這個(gè)領(lǐng)域是領(lǐng)先的，但這可能是針對(duì)應(yīng)用方面。在我看來，許多大學(xué)（科羅拉多大學(xué)博爾德分校、斯坦福大學(xué)、麻省理工學(xué)院、羅切斯特大學(xué)）正在這些領(lǐng)域進(jìn)行大多數(shù)世界級(jí)的研究。
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I remember pencils and paper. I miss the good old days
我記得鉛筆和紙，我懷念過去的美好時(shí)光
原創(chuàng)翻譯：龍騰網(wǎng) http://nxnpts.cn 轉(zhuǎn)載請(qǐng)注明出處
Well, actually there's been a lot of progress, just not as general purpose CPU computing, but in fiber optic communications switching (it's pretty much right under everyone's nose). :)
嗯，實(shí)際上已經(jīng)有了很大進(jìn)步，只是不是通用的CPU計(jì)算，而是光纖通信交換方面(它幾乎就在每個(gè)人的眼皮底下進(jìn)行)。
原創(chuàng)翻譯：龍騰網(wǎng) http://nxnpts.cn 轉(zhuǎn)載請(qǐng)注明出處
@davestorm6718? Correct. Been all-optical switches in comm's networks for over 20 years now. And, optical compute is far different than optical switch.
正確的。作為通信網(wǎng)絡(luò)的全光交換機(jī)已經(jīng)有20多年了。光計(jì)算和光開關(guān)有很大的不同。
TBH as a programmer, I don't understand your domain well enough to understand everything you teach.
But I am curious, and the main takeaway for me from your presentations in general is that it's very inspirational. You have a very good way of explaining things. Thank you for that!
老實(shí)說，作為一個(gè)程序員，我對(duì)你的領(lǐng)域還不夠了解，無法理解你教的所有內(nèi)容。
但我很好奇，總的來說，你們的演講給我的主要收獲是非常鼓舞人心，你解釋事情的方式很不錯(cuò)，謝謝你!
Don't worry, the people who do understand will develop a frxwork or library for you to connect up to, if needed.
不要擔(dān)心，如果需要的話，理解的人會(huì)開發(fā)一個(gè)框架或庫供您連接的。
This is an amazing way of injecting a lot of data into a silicon microchip so you can actually have faster processing. Heck, we can even bring back ring-loops as memory, store it at the GHz range inside a loop of optic fiber. Terabytes of memory faster than DRAM.
Forget about using it for computing, just moving data and storing data are already amazing. Modern CPU/GPUs already spend most of their time just waiting for data to come to their caches. The rate at which computing can happen is severely restricted by memory and bandwidth speeds.
這是一種將大量數(shù)據(jù)注入硅晶片的神奇方式，這樣你就可以有更快的處理速度。見鬼，我們甚至可以將環(huán)路作為記憶，將其存儲(chǔ)在光纖環(huán)路中，頻率為千兆赫茲。兆兆字節(jié)的內(nèi)存比動(dòng)態(tài)隨機(jī)存取存儲(chǔ)器（DRAM）快。
忘記用它來計(jì)算吧，僅僅是移動(dòng)數(shù)據(jù)和存儲(chǔ)數(shù)據(jù)就已經(jīng)很了不起了，現(xiàn)代CPU/ gpu已經(jīng)花費(fèi)了大部分時(shí)間等待數(shù)據(jù)到達(dá)緩存。計(jì)算速度受到內(nèi)存和帶寬速度的嚴(yán)重限制。
You make a good point, this "chip" and similar devices might be better suited for data transfer and communications (not sure about "data storage") than for typical computing. At least perhaps for the time being.
你說得很好，這種“芯片”和類似的設(shè)備可能更適合于數(shù)據(jù)傳輸和通信(不確定是否可以“數(shù)據(jù)存儲(chǔ)”)，而不是典型的計(jì)算。至少暫時(shí)是這樣。
I still remember the first time I allocated a Tb of memory on our HPC, the sense of power was awesome.
Still took half a day to process my data though :(
我還記得第一次在我們的高性能計(jì)算機(jī)上分配1tb內(nèi)存時(shí)，那種強(qiáng)大的感覺太棒了。
不過我還是花了半天的時(shí)間來處理我的數(shù)據(jù)
They are "storing" the data feeds in optical fiber BEFORE processing them.
在處理數(shù)據(jù)之前，他們將數(shù)據(jù)“存儲(chǔ)”在光纖中。
Was looking forward to your take on this paper. Thank you
我很期待你對(duì)這篇論文的看法。謝謝你！
Your channel seems a bit ahead of its time. Keep up the good work, you're great !
你的頻道似乎有點(diǎn)超前。繼續(xù)努力，你很棒!
This could be streamlined AI.
這可能是簡(jiǎn)化的AI。
Complexity of systems increases the probabilities of errors and breakdowns.
系統(tǒng)的復(fù)雜性增加了出錯(cuò)和故障的可能性。
SAW (Surface Acoustic Wave) devices are conceptually similar. The interaction and coupling of different energy types. Resonators, couplers and transmission lines are designed into the system to accommodate each energy type.
Reliable low power laser emitters are readily available from the fiber optic communications industry.
For photonics to expand there needs to be a toolbox of active elements which can be simulated.
Much like the inductive, resistive, and capacitive components in electronics.
表面聲波（SAW）設(shè)備在概念上是相似的。不同能量類型之間的相互作用和耦合。諧振器、耦合器和傳輸線被設(shè)計(jì)到系統(tǒng)中，以適應(yīng)每種能量類型。
可靠的低功耗激光發(fā)射器可以輕松地從光纖通信行業(yè)中獲得。
為了擴(kuò)展光子學(xué)，需要有一套可以模擬的活性元件工具箱。
就像電子學(xué)中的感性、阻性和容性元件一樣。
You remind of one of my old professors. She was an older woman and she was a wonderful professor and I'm very fond of her. She taught Data Structures and algorithms. Thanks for the breakdown of this. It's very clear and understandable.
你讓我想起了我以前的一位教授。她年紀(jì)比我大，是個(gè)很棒的教授，我很喜歡她,她教授數(shù)據(jù)結(jié)構(gòu)和算法。謝謝你的分析,非常清晰易懂。
Good to see a sober assessment of the developments. Eager to see systems that exploit the full parallelism possible with optical computing.
很高興看到對(duì)事態(tài)發(fā)展的冷靜評(píng)估?？释吹侥軌虺浞掷霉鈱W(xué)計(jì)算的并行性的系統(tǒng)。
Me no good at science. Your presentations are so clear, however, that even those of us without great scientific backgrounds can garner the gist of your messages. Really, really, well done. Thank you.
我不擅長(zhǎng)科學(xué)。然而，你的演講是如此清晰，即使我們這些沒有科學(xué)背景的人也能領(lǐng)會(huì)你信息的要點(diǎn)。真的不錯(cuò)，謝謝你！
I'm super excited to see the analog computing applications of photonics, especially around the matrix multiplication arenas. I can see it being a game-changing step to the next revolution of tech
我非常興奮地看到光子學(xué)的模擬計(jì)算應(yīng)用，特別是在矩陣乘法領(lǐng)域。我認(rèn)為這將是下一個(gè)科技革命的轉(zhuǎn)折點(diǎn)
Thank you for another interesting video. I look forward to the day when the technology is perfected for photonics. I think it'll be history making.
謝謝你又一個(gè)有趣的視頻。我期待著光子學(xué)技術(shù)完善的那一天，我想這將創(chuàng)造歷史。
This is crazy technology if they can master it, because of lights ability to be split into different wavelengths or colors this will significantly increase data storage and speed, it'll turn silicon valley into the stone age
如果他們能掌握這項(xiàng)技術(shù)，那就太瘋狂了，因?yàn)楣饪梢员环至殉刹煌牟ㄩL(zhǎng)或顏色，這將顯著增加數(shù)據(jù)存儲(chǔ)和速度，這會(huì)讓硅谷回到石器時(shí)代。
In the blx of an eye...thanks again for a great glimpse at the future.Cheers.
一眨眼的功夫…再次感謝你讓我看到了未來，謝謝。
I think the next age of innovation is sort of dependant on breakthroughs in Material Sciences, it is the key that will unlock a whole lot and every thing will just fall in to place and we will move faster and much further.
我認(rèn)為下一個(gè)創(chuàng)新時(shí)代在某種程度上取決于材料科學(xué)的突破，它將是解鎖許多東西的關(guān)鍵，一切都將就位，我們會(huì)發(fā)展得更快更遠(yuǎn)。
Photonics is very intriguing. It’ll be very interesting to see how it goes.
光子學(xué)非常有趣?？此趺窗l(fā)展會(huì)很有意思。
I remember when this sort of thing first appeared decades ago. My thought then was that it may be fast but it still needs to interface to regular electronics at some point ant that would be the bottleneck.
我還記得幾十年前這種東西第一次出現(xiàn)的時(shí)候。我當(dāng)時(shí)的想法是，它可能很快，但在某些時(shí)候，它仍然需要與常規(guī)電子設(shè)備接口，這將是瓶頸。
Imagine photonics and quantum computing...
想象一下光子學(xué)和量子計(jì)算
原創(chuàng)翻譯：龍騰網(wǎng) http://nxnpts.cn 轉(zhuǎn)載請(qǐng)注明出處
I learn a lot from you! thanks for posting these videos!
我從你身上學(xué)到了很多!謝謝你上傳這些視頻!
I remember University of Crete, in Greece, had breakthrougg research on this type of computing, 20 years ago.
我記得希臘的克里特島大學(xué)，在20年前，對(duì)這類計(jì)算進(jìn)行了突破性的研究。
There have been recent breakthroughs in
1 AI
2 Quantum
3 Photonics
Now imagine an AI running on a Photonic Quantum computer!
It would make all our existing computers as useful as handheld calculators.
最近在這些領(lǐng)域有所突破：人工智能、量子、光子學(xué)
現(xiàn)在想象一下在光子量子計(jì)算機(jī)上運(yùn)行的人工智能!
它將使我們現(xiàn)有的所有計(jì)算機(jī)都像手持計(jì)算器一樣有用。
Doesn't even need to be entirely photonics. Specially if later we develop a way of fusing photonics and electronics.
You can do a lot of computing in parallel, so the latency in electronics wasn't a problem, the problem is always the von-newman bottleneck, the bandwidth at which you can inject data into a silicon chip.
For ex, H100 TPU can do merely 3.35TB/s of data transfer internally. It does 51 Teraflops (FP32) but because it can only transfer 250GB/s from memory, it doesn't get even closer to that.
The H100 has 456 tensor units, if you could feed data without ever having to wait a single clock cycle, you would need at least 153TB/s.
But if your chip can do 1 multiplication per clock cycle (*1) at 3.5Ghz, that would at least consume 0.336 TB/s for a single unit, or 153TB/s total.
So if you could feed data at the rate it can consume, you can easily do 50 times more computing. You can do 2.5 Petaflops instead per chip.
(*1 - tensor cores in the volta actually have 5 stages, those at least use 5 cycles per "instruction", I'm considering 3 floats inputs)
But there's no way to inject that much data into a silicon chip, the die size would have to be huge for the amount of pins it would need to have. Maybe some IBM mainfrxs can do that. 150TB/s is a lot of data.
Now with photonics, that's a possible optimization, far away (aka, in the same server, but not on the same PCB) you can have terabytes of RAM in parallel feeding data to a microwave emitter and then to a light pipe and then to a single microchip. (it would still need to have another back-converter to microwave and a receptor to feed the data to the silicon above, but that would be flip-chip interconnect instead)
Also you can now make it even denser with much more tensor units and remove all the cruft used for managing caches, you don't need caching or even registers, its pure computing, data in / data out.
You can even do crazy things like active cooling inside a microchip if you don't have so much die size being spent on interconnect or cache.
甚至不需要完全是光子學(xué),特別是如果以后我們發(fā)展出一種融合光子學(xué)和電子學(xué)的方法。
你可以并行進(jìn)行大量的計(jì)算，所以電子學(xué)中的延遲不是問題，問題總是馮·諾依曼瓶頸，你可以將數(shù)據(jù)注入硅芯片的帶寬。
例如，H100 TPU內(nèi)部只能進(jìn)行3.35TB/s的數(shù)據(jù)傳輸。它可以進(jìn)行51 TeraFLOPS（FP32）的計(jì)算，但由于它只能從內(nèi)存中傳輸250GB/s的數(shù)據(jù)，它甚至無法接近這個(gè)速度。
H100有456個(gè)張量單元，如果你可以在不必等待一個(gè)時(shí)鐘周期的情況下提供數(shù)據(jù)，你至少需要153TB/s的速度。
但如果你的芯片可以在每個(gè)時(shí)鐘周期進(jìn)行1次乘法（*1）計(jì)算，以3.5GHz的頻率運(yùn)行，那么至少會(huì)消耗0.336TB/s的速度用于單個(gè)單元，或者總共153TB/s。
所以如果你能以它可以消耗的速度提供數(shù)據(jù)，你可以輕松地進(jìn)行50倍的計(jì)算。每個(gè)芯片可以進(jìn)行2.5 Petaflops的計(jì)算。
（*1 - Volta中的張量核心實(shí)際上有5個(gè)階段，至少每個(gè)“指令”使用5個(gè)周期，我考慮的是3個(gè)浮點(diǎn)輸入）
但是沒有辦法將那么多數(shù)據(jù)注入硅芯片，芯片尺寸將不得不非常大，以容納它需要的引腳數(shù)量。也許有些IBM大型機(jī)可以做到這一點(diǎn)。150TB/s是很大的數(shù)據(jù)量。
現(xiàn)在有了光子學(xué)，這是一個(gè)可能的優(yōu)化，你可以在遠(yuǎn)處（即在同一臺(tái)服務(wù)器中，但不在同一塊PCB上）擁有數(shù)TB的RAM并行提供數(shù)據(jù)給微波發(fā)射器，然后通過光波導(dǎo)到單個(gè)微芯片。（它仍然需要另一個(gè)反向轉(zhuǎn)換器將數(shù)據(jù)從微波轉(zhuǎn)換回光波，以及一個(gè)接收器將數(shù)據(jù)饋送到上面的硅芯片，但這將是倒裝芯片互連）
你甚至可以使它更密集，擁有更多的張量單元，并移除所有用于管理緩存的冗余部分，你不需要緩存甚至寄存器，它是純粹的計(jì)算，數(shù)據(jù)輸入/數(shù)據(jù)輸出。
你甚至可以在微芯片內(nèi)部進(jìn)行主動(dòng)冷卻，如果你沒有那么多的芯片尺寸被用于互連或緩存。
Handheld calculators are still useful Especially the ones that use solar for power. I still have mine from decades ago, that works, using LCD and a small solar panel.
手持計(jì)算器仍然很有用，尤其是那些使用太陽能發(fā)電的計(jì)算器。我還留著幾十年前的那臺(tái)，用的是液晶顯示器和一個(gè)小太陽能電池板。
I don't think a photonic quantum computer would benefit from many of the advancements in classical photonic computing.
I could be wrong, but I imagine quantum vs classical photonics is a whole different beast.
But yeah, photonics could certainly speed up AI if it gets good enough, and quantum computers make AI go crazy, but that's probably further away. We'd probably have to relearn a lot of what we know about training classical AIs too.
我不認(rèn)為光子量子計(jì)算機(jī)會(huì)從經(jīng)典光子計(jì)算的許多進(jìn)步中受益。
我可能是錯(cuò)的，但我想象量子與經(jīng)典光子學(xué)是完全不同的領(lǐng)域。
不過，如果光子學(xué)足夠好，它當(dāng)然可以加速人工智能的發(fā)展，量子計(jì)算機(jī)可以讓人工智能變得瘋狂，但這可能還有一段距離。我們可能還需要重新學(xué)習(xí)許多關(guān)于訓(xùn)練經(jīng)典人工智能的知識(shí)。
原創(chuàng)翻譯：龍騰網(wǎng) http://nxnpts.cn 轉(zhuǎn)載請(qǐng)注明出處
i believe they have a long road ahead Anastasii but i think they might have a few good innovations, theoretically it is exciting easyer to understand good job !
我相信他們還有很長(zhǎng)的路要走，但我認(rèn)為他們可能會(huì)有一些很好的創(chuàng)新，理論上這是令人興奮的，更容易理解，干得好!
The problem with all forms of analog computing is storing results for multi step computations or to use those results later. We can easily do this with digital circuits. The problem with analog computing is that you need to convert the analog signals to digital to store the result, then convert them back to analog to process further.
The additional hardware and power consumption to do this conversion greatly out weighs all the benefits. There needs to be a breakthrough in how we can convert or store analog results in order for this technology to be beneficial.
所有形式的模擬計(jì)算的問題都是為多步計(jì)算存儲(chǔ)結(jié)果或稍后使用這些結(jié)果。我們可以很容易地用數(shù)字電路做到這一點(diǎn)。模擬計(jì)算的問題在于，你需要將模擬信號(hào)轉(zhuǎn)換為數(shù)字信號(hào)以存儲(chǔ)結(jié)果，然后將其轉(zhuǎn)換回模擬信號(hào)以進(jìn)一步處理。
進(jìn)行這種轉(zhuǎn)換的額外硬件和功耗大大超過了所有的好處。為了使這項(xiàng)技術(shù)有益，我們需要在如何轉(zhuǎn)換或存儲(chǔ)模擬結(jié)果方面取得突破。
I have been saying photonics is the ultimate in processing for 20 years, finally good to sees it's starting to take hold.
20年來，我一直在說光子學(xué)是處理技術(shù)的終極，終于很高興看到它開始站穩(wěn)腳跟。
so photonic chips are becoming more possible, interesting. we do indeed need a new breakthrough in technology in order to get more significant improvements per generation. feels like we are hitting a ceiling past few years
所以光子芯片變得越來越有可能，越來越有趣。我們確實(shí)需要在技術(shù)上取得新的突破，以便每一代都能取得更大的進(jìn)步。感覺我們?cè)谶^去的幾年里觸到了天花板
i used to design ring resonators, tapers and couplers.
我曾經(jīng)設(shè)計(jì)過環(huán)形諧振器，錐形器和耦合器。
The big problem here is not the chip - it is manufacturing. The idea is amazing, but they need to get the whole chain up for fabs to implement that in mass, then someone designs the chips. The later is quite easy - but the supply chain for the whole fabs will likely take years. There is a hugh push - you can sell those chips VERY expensive for some time, because - at the end - you are 1000 times faster, AND you also use a lot less energy (the later being money spent and infrastructure requirements
這里的大問題不在于芯片，而在于制造。這個(gè)想法很神奇，但他們需要整條生產(chǎn)線，讓晶圓廠大規(guī)模實(shí)施，然后有人設(shè)計(jì)芯片。后者比較容易，但整個(gè)晶圓廠的供應(yīng)鏈可能需要數(shù)年時(shí)間。這是一個(gè)巨大的推動(dòng)力——你可以在一段時(shí)間內(nèi)以非常昂貴的價(jià)格出售這些芯片，因?yàn)椤罱K，你的速度會(huì)提高1000倍，而且你使用的能源更少(后者是資金支出和基礎(chǔ)設(shè)施需求)
Thanks for the video. Photonics may be useful in the future, but as you explained, this technology must go through considerable development before it is ready to replace electronics. I've been able to watch the progress of lithography, including several promising technologies which never reached large scale commercialization.
感謝您的視頻。光子學(xué)在未來可能很有用，但正如你所解釋的，這項(xiàng)技術(shù)在準(zhǔn)備取代電子學(xué)之前必須經(jīng)過相當(dāng)重要的發(fā)展。我見證了光刻技術(shù)的進(jìn)步，包括一些從未實(shí)現(xiàn)大規(guī)模商業(yè)化的有前途的技術(shù)。
Very cool, photonics is very exiting now that we are are approaching the limit for silicon.
非?？?，光子學(xué)非常令人興奮，現(xiàn)在我們正在接近硅的極限。
Anything we can do to lower power usage for AI will be good for the planet. With photonics I question if the logic density can ever compete with what has been achieved with silicon gates. I'm not sure quantum computing will be the next big thing, since I think the next big thing will probably be discovered using AI, and it will come up with an approach we haven't even considered before!
我們能做的任何降低人工智能能耗的事情都對(duì)地球有好處。對(duì)于光子學(xué)，我懷疑邏輯密度是否能與硅柵所取得的成就相競(jìng)爭(zhēng)。我不確定量子計(jì)算是否會(huì)成為下一個(gè)大事件，因?yàn)槲艺J(rèn)為下一個(gè)大事件可能是使用人工智能發(fā)現(xiàn)的，它將提出一種我們以前從未考慮過的方法!
Actually, It's much faster than 1000 times when you start breaking it down into different colors....yet future, but we will get there.
實(shí)際上，當(dāng)你開始把它分解成不同的顏色時(shí)，它比1000倍快得多....雖然是未來，但我們會(huì)實(shí)現(xiàn)的。