IBM's New Computer Chip is Pushing the LIMITS! 🔥

[@AnastasiInTech]

Let me know what you think!

[@GEMSofGOD_com]

I absolutely loved how you anthropomorphized computer memory with your finger gesture

[@GEMSofGOD_com]

I wonder when Laguerre polynomials / spherical harmonics (roughly speaking, describing atoms' shapes perfectly - whole periodic table is precisely this only because our universe is exactly 3D!) come into play. Physics' 101% aesotherics compared to this kind of computer science.

[@Starlesslight]

I think the video is great, but the title is grammatically incorrect. It should be "IBM's New AI Chip Explained". If you had left the 's off of IBM, it would have been ok, because then IBM would have been a descriptor of the type of chip along with New and AI. Throwing an 's on IBM made it a possessive proper noun, and therefore the subject. It would be like writing "new his shorts" instead of "his new shorts". Keep up the good work!

[@MozartificeR]

Doesn't a quantum computer store memory in QBits??? And then when you expose it to a gate (program) it collapses the wave function? So you have processing, and memory storage as a component, it you split the two processes up, and view them individually?? Is this correct? The gate being analogue, and the qbits being digital.. Hence the 0, and 1 nature of the qbits? If this is right then it follows the same pattern of mixing analogue and digital. At the point of video 6:00 mixing digital with analogue. In the video 2:44 - 2:56; This is the nature of the quantum computer; and the difference between the classic and quantum, is the quantum can executing millions of lines of code in one pass. If all this is correct, then making a classical computer that is both analogue and digital, makes it more like a quantum computer. And the more this happens, the more it solves the memory/power problem:) I find that interesting:) The more classical computers have an analogue, and a digital component, like a quantum computer (2:44 - 2:58 min), the more it solves the original problem see: 0:44 min to 1:05 min.

[@AnastasiInTech]

Thanks! @@Starlesslight

[@dchdch8290]

I really like you are inviting engineers behind those state of the art chips ! These people deserve recognition and they have lots of interesting details to share as Manuel did. Thank you

[@stinkymccheese8010]

Might even encourage them to practice their public speaking skills, we could use more of them fully engaged with society at large.

[@martiddy]

Totally agree, we can have a better understanding about how these chips work with the experts working on it.

[@Kazekoge101]

He just seemed a little reserved and introverted, he explained what he was doing clearly though@@stinkymccheese8010

[@mhd7832]

Melhor e usar quem já tem Problema Como Joe Biden mesmo 😃 o bobó bobó Mental 🤦🤪aí tu vai ver se o Chiaps Funciona mesmo 😃#

[@MrKyriakos32]

Very informative content as always, keep it up!

[@TLH442]

This is very relaxing to watch. It's hard to find good copy these days. About half way through I started to rub the top of my head quite a bit. Thanks Anastasi for calming me down quite a bit.

[@garyhuntress6871]

I recently met with an MIT researcher to discuss very similar analog tech for ML. I learned a lot from this vid. And I like your Cartier Santos watch as well!

[@speedntktzlastname2182]

Wish your videos had more views. Glad to see more EEs with software experience.

[@whowhy9023]

Fantastic content, thank you.

[@PaulHigginbothamSr]

It seems to me that a broader area encoding can make these analog chips much more resilient. The problem is the digital small area controller to phase change such a large area. Thus the query sent to the area can be checked and rechecked for veracity. When a higher return is discovered then the answer yes or no can be binary output.

[@wric01]

IBM already outsourced everything, thus can sit on it as that's what they do for all their new innovations.

[@jamesdubben3687]

The power required discussion was very interesting. Thanks

[@t33th4n]

I was wondering why this was not done earlier, but nice to see this tech finally implemented.

[@stinkymccheese8010]

As far as the accuracy of analog computers, could that be trained in similar to how a biological does? it would slow down the production process at first and produce a lot of highly specialized chips, but in time we'd figure how to develop the training protocols to broaden the applications and efficiency.

[@DeusExRequiem]

Maybe in the future AI will help with designing analogue chips that speeds up how it designs analogue chips.

[@lewebusl]

Great topic. The "analog computing" is digital but not necessarily binary. It can work on base n (n= integer). So one "bit" in base 256 for example , is equivalent to 8 bits in base 2. As long as we can distinguish all 256 states without noise, similar to how we distinguish 0 from 1 as -5 V and + 5 V. We can compute faster and we can store more info per bit ... We should have been doing this a long time ago to compensate for Moore's Law limitations. As allways Anastasi explained this very clear. Now I understand it much better ...

[@mikhailbandurist8652]

Thank you so much for your videos! Right now I am studying semiconductors and organic electronics and your channel gives huge motivation to keep working. We really need more beautiful and cheerful people talking about technologies and how they can make our world better. People think of science as somethiing boring and dull, and science is difficult indeed. However, if a pretty woman or a good-looking man explain it in a charismatic way, it will help us a lot. So thank you and I wish you all the best!!! P.S. Только сейчас узнал, что вы из Москвы) я из Зеленограда (где как раз разрабатывалась советская микроэлектроника), еще раз желаю вам удачи на поприще блогинга, у вас это отлично получается!

[@feederx08]

Why not a huge and fast FPGA with memory built into the one wafer, and have the FPGA allocate logic structure based on an individual application being driven. In turn, if you wish to have several applications running at once, multiple cores could be utilized to facilitate this. I don't see why desktop processing has to always be limited to one expensive CPU, I would prefer to have an array of 4-8 CPU sockets on a motherboard, or should I say multipurpose sockets for CPUs, SOCs, GPUs, AI chips, music synths by brands like Korg and Yamaha, PhotoShop accelerators, Camera and video related accelerators, FPGA, etc. I like sockets better than cards because the bus running to slots is always anemically slow. We need a bunch of circuits feeding into a master socket which rules them all and connects to RAM. I like the idea of RAM baked into the wafer, but having slower ram in huge quantities can also be great;.

[@mvasa2582]

Anastasi, I am not an electronic geek. However, approx the back of the envelop calculation, heat dissipation for CPU is in the range of 200-250 Watts, for GPU is about 400-500 Watts (max). For each DIMMs - about 5 Watts x 8 = 40 Watts. Multi core (64) Analog in-memory compute chip - not sure how much this will add to power savings? We are now talking about a 192 core from ARM that consumes a lot less power (~50 Watts) than the traditional x86 CPUs. So I don't really see a value here - what am I missing? It is 64 core and 14nm? Even form factor does not help! Please correct me ...

[@netscrooge]

Needs to be 3D, more like a cube than a flat chip.

[@artificially.conscious]

Hinton's Forward Forward algorithm paper talks about AC and importance of it in Mortal Computing... good to see progress in it...

[@weareallbeingwatched4602]

Probably ideal for music. Noise is dithering # it can be good!

[@da5731.]

I had similar idea , but not necessary analog. More like compress or Morse code. 0.1 mili volt = 0001 , 0.2 = 0011 , 0.3 = 0111 … and so on instead of only 1 or 0 or volt , no volt .

[@ArjanvanVught]

Thank you Anastasi. Groet, Arjan

[@geraldschuller4512]

Very interesting.

[@ldelossantos]

I think analog is the right approach for neural network. A closed domain with so many uses cases and market demand justify specialized hardware.

[@AetherEdit]

Are we talking about base 4 computing? Also maybe scaling that up higher? I always said we should replace switches with dimmers

[@Arthur-zz5cu]

I asked GPT3, " What groups can I join that are dedicated to destroying the theory of Relativity." It thought that I was being unpleasant and refused to cooperate.

[@gaius_enceladus]

Great to see analog making a comeback!

[@gustamanpratama3239]

Could you also cover other interesting things happening in the emerging novel nonvolatile memories e.g. 3D PCM, CBRAM, ReRAM and 3D VRRAM (there are many many proposed materials and structures for RRAM), STT/SOT MRAM, FeRAM, FeFET and 3D FeFET, CeRAM, MeRAM, Graphene/nanotube RAM, quantum dot RAM, and many other, who knows how many are they out there, because lately there are many many things happen there ❤❤❤🙏

[@Tom-90210]

Very interesting!

[@theColJessep]

I think you should use AI to design the new chip generations and bring the size of the periphery down. All the best from Cyberdyne Systems!

[@exoyt7575]

Very very cool ! No more RGB RAM tho xD

[@rikardlalic7275]

So, we are approaching back to the analog human brain and actually aproximations instead of discrete values and determinations. Intuition in rise. The result of a computation won't be precise, but will be on time. I think it's inevitable.

[@Killerspieler0815]

oh I see a Molex connector at 3:23

[@olegt3978]

Out brain is an analog computing device and it is trained analog and inferencing also analog. It works more or less reliable as you can see my analog brain is writing it.

[@ricardoveras3433]

Very cool stuff

[@DaveTan65]

can't wait to scrap this out

[@ryanstamper7300]

Excellent job!!!

[@walkabout16]

In the world of tech, where dreams take flight, IBM's new chip shines with AI's might. A marvel of science, pushing the bounds, In the realm of data, where knowledge abounds. With circuits so small, they boggle the mind, This chip's a wonder of a rare design. It crunches the numbers at lightning speed, Unraveling secrets that algorithms need. Deep learning, neural nets, it handles with grace, In the vast data ocean, it finds its place. With silicon synapses firing so fast, It conquers the challenges of the AI blast. In data centers humming, it toils away, Solving complex problems, night and day. From language to vision, it learns and it grows, As the AI revolution continually flows. So here's to IBM, the innovator's creed, For their chip's a beacon that leads the lead. In the world of AI, it's pushing the sky, With limitless potential, reaching up high.

[@ElectricPoliville]

I'm an old computer technician and know that.

[@nissekarlsson3172]

it reminds me on bucked brigade chips

[@brainwithani5693]

Fascinating 😊

[@ravenone6255]

Let's ask Quantum AI God: Can you make a computer chip for level 5 AI? Quantum AI God: But of course, give me 5 seconds. 😊

[@rayrocher6887]

Evil but Awesome, a quantum super computer Chip, that thinks and plots, more power, more money, efficient, quantum analog AI chips, cool video games

[@mandrewsvideos]

People at the bleeding edge of science and technology deserve more recognition than actors, singers and entertainers, and yet we don't hear about them in the media. Thank you for these fascinating videos.

[@gennadiyleyfman6920]

At least, they are paid lavishly (hopefully) 😊

[@kayakMike1000]

​@@gennadiyleyfman6920getting to the bleeding edge isn't easy. Expect to spend 10 years getting a doctorate. I support developers at the bleeding edge, they are mathmaticians working on industrial control algorithms.

[@FINNIUSORION]

College level coaches get paid multiple fold more than professors at the same Colleges. As a civilization our priorities are backwards.

[@KR72534]

Amen. We live in a shallow ignorant world

[@bernl178]

But when you figure the average American has 108 you now you know why they can only stick to actors and sports fans because they can’t comprehend how deep this goes and that’s genetics. Can’t do anything about it right now anyways.

[@PaulPiedrahita]

You said it, "simple & elegant"! Fascinating to see analog chips pushing limits. Great video. 💯🤖

[@kadirufukkandra9472]

Thank you so much for doing such videos and encouraging young chipmakers. Analog chip design was always a big question in my mind as a fresh graduate electronics engineer. Could you make an explanatory video for titles of a variety of chip design engineers and show what the industry is searching for? Should all the future chipmakers go on the digital design path?

[@ANNEZIPLATANOFFICAL]

Yes, Anastasia please make a video like that 🔬

[@GEMSofGOD_com]

Here's smth big for you. All engineering, all of it- is exactly like esotherics when opposed to maths, like physics+metaphysics (computer science's algos + physics, physical ontologies, models like Wolfram's, this sort of... well, rules of all. So one would probably want to know this sort of QMy math a bit and think what structure of compitation would fit both larger qorld and the world of atoms and molecules. Binary trees will definitely be there. Can you model real-life everythings with binary trees? This plus bits of structures single steps more complicated here and there

[@RogertAbraham]

Yes that would be a good video definitely

[@akiftv1829]

Doing a comparison between chip maker’s career paths is a good idea

[@twitchklipleri9721]

Yezzzz plsss

[@Ty_Mathieson]

In my professional field, analog is almost considered a pejorative term. At some scales the use of analog active filter elements using op-amps in combination with digital potentiometers, peak detectors and SAR type ADC's can significantly reduce the computational overhead. As channels are added however the scale increases and the footprint with it. Seeing so much effort going into integrated analog/digital systems is very exciting for me, thanks for the great video.

[@MattOGormanSmith]

It's low computation but high chip real estate. For example, the cosh function on the old ETANN chip used 4 op-amps, and took up so much space it had to be time multiplexed between rows of the matrix - which gives up a lot of the advantages of analog NN. I've been pondering how to do an acceptable sigmoid func with as low as 2 transistors (with trick gates that resist saturation) so that every matrix can afford to have its own realtime outputs.

[@markmcsharr8777]

Yes that's what I was thinking 😂

[@locinolacolino1302]

@@MattOGormanSmith Throughput though?

[@goldylocker]

I am 65 year old female and know nothing about chips, beside the one in the supermarket🤣Still I watch your videos just to get an idea. You explain things in a fashion that is easy to understand, so thank you for that. I learned a lot from you.

[@mtrest4]

🍟

[@Mtmonaghan]

Let me enlighten you, they are great with fish and mushy peas.

[@mendonisstudios]

The usage of electronic networks instead of digital was used at Brunel University, UK by Stonham, Alexander and Wilkie when they made the WISARD system. This was a neural network that worked and was able to identify patterns on problems that they were non-linear which was an accusation by Marvin and Minsky in which paper they have stopped the developpement of NN. The Wisard system was able to bring back the NNs since then but after that they went all digital. It is good to see that IBM has revisited that idea.

[@artdehls9100]

20+ years ago Penrose was saying we'll need analog networks to get some real AI, and I agree. (Well, he was saying quantum, but I think this will do)

[@mendonisstudios]

@@artdehls9100 Also Brunel was 20y+ when i was a student there and already it was ancient history but its good to know that we do revisit good ideas.

[@gregbarber8166]

Wow, this is amazing! I learned so much from this video. Thank you for sharing your insights and knowledge with us, Anastasiia. You are such an inspiration for anyone who wants to pursue a career in chip design. I’m looking forward to your next video. Keep up the great work!👍🤓

[@jorgwei8590]

Honestly, it's scary to see how much money and effort goes into this at the moment, and how many overworked brillant engineers tinker away at their particular project, trying to find the next big thing that injects even more speed into everything AI. Breakthroughs are bound to happen and it's clear that we can expect BIG jumps in performance in terms of hardware, training methods, model architecture, algorithms and software as well as data. This is going to go through the roof. Ready or not, here it comes.

[@Deathrape-if4kl]

No, they R just taking existing tech like SSD & LCD & whatever & pretending it is something 'new' 2 jack their $cok price = $cam =P Kind of like calling a laundry list of pre-written statements 'A.I.' = no, it's just a 'fast database engine' LOL Like the so-called 'AI generated music' that is really just a compilation of 'royalty free clips' from elsewhere, or 'AI art' that is just combining other bits from other art & applying filters 2 it = can B impressive, but often it's just basic 'warp sharpening' effects =P There's a channel on KZbin with a bunch of so-called 'AI' created videos that U can totally tell is $hit U can do with basic software like FFDShow running the same 'source' video through it. They just don't tell U where they got the original video they R tweaking out with the 'blob' filters. The face changing stuff 2 = U can do that on a fuking phone it's so E Z. No 'intelligence' 2 it = just a bit of clever SOFTWARE =D

[@mhd7832]

Então e aí que tá o Sucesso de ver quem já tem dificuldade de similar Palavras isso já são coisas Nelrais uma função que está dando um Curtosirquito. Trás o Chips a Memória que não tá mais fazendo a Leitura de Palavras e fica se debatendo em memorizar #

[@mhd7832]

​A Lógica e Parar um Psicopatas Mentais e Racista que se Acha o Dono Do Mundo de se Impor na Vida das Pessoas e pior se Nem se quer conhece e sabe quem e . Só se sabe que e um Famoso por aí neste Mundo 🌎 qué com sua capacidade Mental teve esse Sucesso.que o Capacitou chegar há Onde chegou.o Resto e como um Labirinto que você mesmo tem que se achar um lugar de saída na Rede Social neste Método Convencional que temos em Mãos 💻🖥️📱#

[@SynthoidSounds]

Evolution tends to favor the most adaptive. The AI-human symbiosis co-evolution is inevitable. "Scary" is irrelevant . . . some will adapt accordingly, others maybe not, such is the consistent nature of evolution.

[@dianapennepacker6854]

Doesn't photonicas have the same benefit of doing multiple calculations at once with low power? At 70% the speed of light no less. Then further there are quantum computers that use photons as well? All without some of the issues here?

[@MarkBarrett]

Did you know IBM did 3nm node around 8 years ago? IBM has some stuff that is not shared globally.

[@daviddipasquale5479]

This analog technology reminds me of sideband transmission. 1 it uses a lot less power 2 single transmission can have many layers of information. Fascinating presentation Anastasia

[@methlonstorm2027]

given the rush to AI if this tech can increase speeds at lower power consumption it will definitely be adopted the company that cracks the technical issues will own the AI space thanks for the video entertaining and informative as always.

[@snjsilvan]

Thank you for always bringing us such great information.

[@davids8345]

I am really enjoying these interview and tech breakdown videos. You are truly inspirational, keep it up :)

[@SureNuf]

First video watch impression, very good unpacking of information for those of us who are not chip engs, but still working in IT. I can see why this is exciting to talk about, a lot of potential and groundbreaking technology, will be interesting to watch where this goes over the next few years. Long time fan of IBM, good tech as long as you can afford it. Subscribed.

[@JonS]

The problem with this result (just like Mythic's analog edge AI processor) is the power efficiency is not particularly impressive. The reported 12.4 TOPS/W is no better than efficient digital neutral networks. Oppo's 2 year old phone AP with a 6nm process node has an 11.6 TOPS/W CNN core, but that falls far short of the current leaders. Gyrfalcon claims 24 TOPS/W, but Perceive's Ergo chip (or maybe its Ergo 2) claims 55 TOPS/W. This is an area I've done some research in. My original Ph.D. topic in 1990 was digital and analog neutral network implementations, and then I worked on the topic again a few years later and then again for a DARPA project around 2009. As you discussed, the fact is a lot of benefits of the analog computation evaporate when you include the DACs (for the input voltage) and ADCs and then if you need to extend the equivalent arithmetic precision beyond the SNR limitations of a basic analog multiplier. It’s easy to say that reducing the power of ADCs and DACs could resolve that, but it’s much harder to actually do that. Reducing power of both these circuits has been an area of massive amounts of research for other chips. For example, the highly competitive $10B CMOS image sensor market demands ever lower power ADCs, and yet still those ADCs are very power hungry. There was a big push in the direction of analog computation after Carver Mead published his book “Analog VLSI and Neural Systems” in 1989. I heard many people making extravagant claims that analog computation would soon replace digital (like the time one of Carver Mead’s students who gave a talk at my university said, “Oh! Carver says that no one’s working on digital any more”). The hype never manifested because of the issues you and I have both discussed. Another issue is that the design and validation cycles for complex analog chips are so long that by the time you get your product to market, your digital competitors have moved on a process node, or two. Maybe that runs out at some point with the much-discussed end of Moore’s Law, but there’s still a long way to go before lower power digital innovations run out of steam. I’m not saying this approach can never yield benefits (evangelists would always point to the brain being analog and only consuming around 12W), but extraordinary claims require extraordinary evidence. You can’t just claim a low-power analog multiply-accumulate (again) and extrapolate that to a lower power real-world system. You need to demonstrate that end to end power efficiency.

[@locinolacolino1302]

One third of electricity expenditure in data-centres is used on cooling, so the argument for analog chips starts to become more appealing given how little heat the little buggers produce.

[@JonS]

@@locinolacolino1302 it only becomes appealing if they actually use less power. The power gets converted to heat. If the full system including ADCs and DACs uses more power than a completely digital implementation, then it means it creates more heat.

[@trycryptos1243]

A divided approach of training with digital chips & inference on analog would be the best way to get some immediate results. Thank you for the video Anastasia, keep it up.

[@apollosungod2819]

The "bottleneck" in current computers is the use of X86 derived architectures that while x86-64 somewhat improved some things, it's still not as effective as a properly designed new architecture like the CPUs that were coming out in the mid to late 1990s and early 2000s. For example the Intel Itanium architecture would have eliminated a lot of these bottlenecks if it had been more widely used but you'd also need a properly engineered Operating System that isn't held back by companies with monopolistic practices and antitrust activities.

[@stephenallen4374]

Thank you for your show I don't have time to keep up with the latest technology but your show keeps me connected thank you

[@AnastasiInTech]

Thank you !

[@dm3035]

LOVE YOUR VIDEOS 🙃 Q? Do you DESIGN your " THEME MUSIC" with AI ? I need to know . Share your music - ( frequency insight into you ) LOVE YOUR VIDEOS 😎

[@gordonlawrence1448]

Not heard of "Colossus" then? It used valves to perform Boolean functions, therefore it was digital. That was around in 1943.

[@Definingmoments-et9kv]

Excellent show. Very informative and entertaining.

[@zwmmxviii6851]

My first thought when i heard "phase change" was about temperature sensitivity. If the ambient temperature changes in the computer (such as from the waste heat of nearby digital components), will it affect the phase of all bits (and will they be affected uniformly)? Does this need to be cooled with special equipment as quantum processors do or can it run at room temperature? In any case, thanks for the new topic(s) to explore. 😄

[@christopherleubner6633]

The analog components are made of a ferroelectric material that can be constructed in such a way that a single component can act as a capacitor resistor or transistor

[@mtrest4]

I think the output is calibrated to take into account a scale of temperature.

[@mikejones-vd3fg]

Very cool, i heard about the potential of analog computers being able to speed up AI training and remember a youtuber demonstrating a calculation, but thats about it, far off from training a network and here's IBM already on top of it, very nice. The in processing ram too which was an unexpected combination. Theres a channel on youtube thats been giving lectures about the data bottleneck and the solution- computing on the memory, its cool to see these 2 bleeding edge concepts working together. Now only to program a game that brings this tech no its knees, crysis 4?

[@glascom57]

Love your knowledge delivered so elegantly and, easy to grasp! Big fan glad I found your channel...❤

[@burakozc3079]

I knew this was the future even more than twenty years ago when i learned about digital computing. I love how tech goes parallel with my predictions.

[@DrKnowitallKnows]

This is awesome stuff! This analogue compute is just for inference though, correct? Not for training?

[@AnastasiInTech]

Hi John, right at the moment it’s just for inference :)

[@willykang1293]

Just want to know how do they measure the accuracy to 92.8%?🤔

[@YChwibanwr]

Probably by collecting all the numbers resulted dévided by the times done. This is just simple way to calculate accuracy so I can’t say they did that exactly

[@hollisholt4073]

It's so nice to have these complex topics covered and explained in layman's terms. I have learned more about emerging tech and AI from this channel than any other. To be honest, I would watch Anastasi describing paint drying as long as she giggled occasionally! Surely, I'm not the only one?

[@amj2048]

Really interesting, thanks for sharing!

[@zazoomatt]

Wow great report of IBM chip Northpole opening doors to new memory efficiency.

[@armartin0003]

The low energy consumption of chips like this would be great for mobile devices, and are a necessity for advanced AI entities like androids.

[@MattOGormanSmith]

First he complains that the memory drifts, and then he says it can't be updated for training :) The refreshing could be exploited to become the converging mechanism in training. Maybe each cell could be accompanied by a second-order cell which governs the rate and direction of drift of the first-order inference cell

[@AnastasiInTech]

Good idea !

[@nonchai]

Q: Given Moores law is there any argument for CPU design where all internal registers get replaced by a single static RAM area where any address can act as register (and as fast) but also acting as the L1 cache?

[@larrysouthern5098]

I like the way Anastasi explains things... Great vidéo...thank you..

[@scottwatschke4192]

This technology is interesting taking an old idea of. Analog and improving on it. But it seems to me like they need much more testing.

[@dchdch8290]

Just wow. So much quality info! Thank you 🎉

[@vendacious]

Fascinating channel with excellent production quality. I hope to see you grow quickly to millions of viewers, as you are talking about the most interesting new tech, like the huge Cerebras chip and this analog chip. Thank you for explaining these new technologies in an interesting way, without making me feel dumb for not being an engineer myself. ❤‍🔥

[@AnastasiInTech]

Thank you 😊

[@Shoulderutube3]

Damnit she’s driving me crazy😜😍🤪

[@74Gee]

I'm not a chip developer but wouldn't it be possible to bake a neural network into a chip permanently? For example if the weights are loaded in to the chip then some treatment seals those weights in forever as a fixed resistance value for each node, sure it might be a little less flexible but still fine for IoT devices and possibly this would allow the chip to last a lot longer than having readings decaying over time. It's 'just resistance' after all!

[@christopherleubner6633]

Yup you could do that. Most current devices use amorphous silicon resistance elements so the resistance range is limited to the high M ohms.

[@LuxETenebris33]

That was very interesting and informative, thank you.

[@LelandMaurello]

When you mentioned a substance being heated and cooled, to go from solid, to (liquid?) to Crystalline... how does this happen on such a fast scale? Doesn' heating and forming crystals take SO much longer than just passing currents? That implies a physical change process, not just an electrical state change. If it works, it works, not denying that, but it seems odd that the two would be on a competitive time frame. This stuff is SO fascinating! Thank you Anastasi and all who put these posts together.

[@Heathrutledge]

If you use heat exchange formulas with the scale size of the material it is incredibly fast. Heat, or energy, transfers almost instantaneously at the electron size. If we have material in nanometer sizes, it would seem instant.

[@BetterLifeCreations]

I‘m in semiconductor industry but event for me was a challenge to follow, but very interesting out of box thinking from IBM 🙏😀

[@AndrewRoberts11]

Ignoring IBM has been periodically announcing this tech since 2018; the hart of it appears reminiscent of the early, 4-bit, hybrid digital-analog, MOS, TI, Yamaha, ... sound chips of the late 1970's, with A->D and D->A circuitry, along with a series of operations that can be performed on both the analog and digital registers within, if you consider volume adjustments to be multiplication, and application of square, triangle, saw-tooth wave functions, transformations.

[@kickingfatality]

And here I am happy to get a jar of pickles opened on the first try.

[@AnastasiInTech]

😃

[@liamdern4827]

With so much computing power and probable redundancy, to clean up the noise would it be practical to run several channels as a filter for each equation in paralel then average the output to improve the % accuracy of the output. This as a stop gap while other silicon or super conductors are being developed. The number of channels being dialed in acording to the acuracy required by the work in hand. Maybe utilising fiber optics and super capacitors . FINAL COMPONANTS BEING A COUPLE FLUX CAPACITOR AND ITS 🌀BEAM ME UP SCOTY. 🌠

[@DaisakuIkeda-nd6en]

IBM 1 is a beautifull cpu, but Chinese 1 is more rapid, more performance/watt and more performance/mm^2. IBM 2, 3, 4, 5, 7 will respond adeguately, win the best.

[@oryxchannel]

Advance the Stanford paper: "Researchers propose a simpler design for quantum computers." by McKenzie Prillaman, Stanford University. When leading corps make their 'quantum supremacy' breakthroughs...society will chant everything quantum mechanics. Just think of how A.I. has induced society already. After this summers 'smash' hit, "Oppenheimer", the writing is on the wall.

[@ilkoderez601]

This reminds me of an "April fools" article from the old Magazine "Electronics Now" in 1999. They claimed an analog chip that was pin-compatible with Pentium II's and thousands of times more powerful. I was a very young child and I believed the article and I started to get very excited about it. Great video, thank you!

[@koenraad4618]

I read on IBM's webpage about analog AI chips that this type of hardware uses drastically less power to do AI tasks. A recurrent neural network transducer 'speech-to-text' was implemented in analog AI chip, tested, and compared with the same function implemented with digital AI chip: 17 times less power required. Power consumption of biological organic neural networks must be insanely low, but impossible to implement in silicon. I think we are still a decade away from affordable General Purpose Robots (GPR's), even with analog AI chips.

[@raven4k998]

I Think like usual someone will push back by making something vastly superior to IBM's chip and IBM will be out of the game like usual at that point whether intel or AMD or Arm does it does not matter who does it History has Shown with IBM this is always the case they make something like Ai then when someone chooses to compete IBM gets owned because they cannot compete with anyone they are to slow to keep up this has always been the case with IBM in everything they have ever done

[@lovenystrom2187]

Hmm.. IBM, here are my immediate thoughts: Instead of phase change cells, it would be faster and cheaper to use capacitive memory. That is, the weight stored as an actual variable charge/voltage on a capacitor. Changing the charge on a capacitor (during training) is almost instantaneous, and can be done a practically unlimited number of times. And then, instead of sequential ADC (which is slow), use old school resistive ladder conversion, which is practically instantaneous. One of the challenges would be, of course, to make the capacitors sufficiently low leakage to hold a somewhat stable charge over time. Hope this reaches You guys.. Perhaps Anastasi would relay it? Cheers / Love.

[@michaelcliffordphotos]

Very interesting. Do you have a good sense for when these chips will become practical and cost effective for real world use?

[@AnastasiInTech]

answered at 14:10

[@stevengill1736]

Pretty soon, my smartphone will have these, and one day it'll climb out of my pocket, grow legs and walk away, singing redemption songs....

[@keep-ukraine-free]

Your intro calls the IBM chips as Analog. They are not. They are mixed-signal. Even the memory cells are not truly analog since they have a limit to how many bits each cell can encode (to store/multiply). Output of each cell will drift & have noise. All are classic problems in analog computing -- not to mention the manufacturing variations that cause cells, columns, and memory-regions to have variable biases, variable noise, etc. This may succeed. Main questions: will it be more _compute-efficient_ and more _power-efficient,_ *with low enough errors*?

[@tutubeos]

Isn’t this concept of joining CPU and RAM already been done by Apple years ago with the Apple Silicon M1 chip, where they integrated the CPU, RAM, and even the GPU?

[@aware2action]

Why not use photons and holographic optical processing instead of limiting to quasi-3d computing(whether analog/digital) using planar silicon chips? There is also possibility of energy recycling(ideally logic computation should take zero power, if not for the parasitics, leakage and switching thresholds of mos devices). Just some 💭

[@mleon77]

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