@graxxor
Engineer: AI Needs a ton of ram. Tim Cook: Great, let’s ship our MacBook PRO with 8Gb.
@ivonakis
People: we want better battery life in laptops. Microsoft: we will use NPU to do tons of work in the background. People: disable it to get better battery life.
@KH-lg3xc
- Computer, what big NPU you have! - I need it to better spy on you.
@skygodofallurya
I've transitioned from 'wow, that's kind of cool. Wonder what they'll come up with next?" to "Fuck off with all the AI, please," in about six months.
@int-64
Just when they faced limits with CPU and GPU suddenly out of nowhere appears new NPU thing that you definitely need in every device.
@jorge69696
I already saw laptops with NPUs but 8gb of soldered ram 😂
@ScottAshmead
guess that is why Apple was saying 8G is enough in the recent past so they would have a solid up-sale later
@JamesRoyceDawson
Looking forward to the AI bubble bursting. There might still be some AI that's worthwhile but it's way overhyped and underdeveloped right now
@boltez6507
Dude i must say this is the best video that i have come across that explains the need and differences of various components (such as CPU and GPU) in simple terms.
@Iswimandrun
Yea recall is a huge security and privacy nightmare
@HarisAzriel
This is the best explanation of NPU so far! Other people that have talked about it are either only talking from political/emotional standpoint or from the money aspect. No one has ever discussed it in a practical application angle like you did. Great work, man!
@mrhassell
NPU = not probably used.
@H_Gemei
the realization that 2017 is seven years ago 😳
@teamredstudio7012
I'm impressed that you explained a simple neural network correctly. Not a lot of people actually understand it. There's of course a lot more to it like back propagation and quantisation.
@PhilippeVerdy
NPUs are just specialized units made for s single task: computing multiplication of matrix with very large sizes, by using many parallel multiplications and a final layer of cumulative additions; the whole being surrounded on input and output by "samplers", i.e. functions that adapt/amplify the signals when they are not lienar by nature (this layer can also use matrix multiplication, or specificat operations like "capping" or sigmoid nonlinear conversion for smooth transitions); Then you need being able to schedule all this task efficiently (when the matrix is too large to fit in NPU registers and the number of multipliers and adders, you need to split the matrix to do that *sequentially*). However there are ways to significantly reduce the amount of calculation needed, notably when matrix are sparses or contain many partial replications: All the optimizations are in finding the best way to split these matrixes into submatrix and eliminate those submatrixes that won't contribute to the final additive result: for that you have not only "trained" the matrix, but also designed some threshold that will allow making the maxtric more sparse. As well the hardware acceleration can help automate the discovery of replicated submatrixes, so that you need to compute one of them and not all, and cache the result or reuse it in the scheduling of the pipeline. Once you've understood that: the NPU is not just for "AI", it has many possible uses in various simulations, and for processing massive amount of datas. When you create a AI model (by learning process) what you create is a set of constant parameters that feed the matrixes. Then you can put mulitple layers of matrices used exactly the same way to create other effects (including retroaction for AI using passive learning and adaptation). The difference with GPUs is that NPUs are more general and can use different trained data models, whereas GPU do this for specialized tasks (with hardwired models and specific optimizations, allowing them to do computation on even massively more input parameters, but with little of no retroaction: the matrices they use are small in GPUs to compute their "shaders", computed as small programs running in parallel with the same kind of data, but this is the massive rate of inputs and outputs needed to produce high resolution image at high farme rates that changes radically things compared to NPU). A GPU may integrate a NPU unit however for solving some local problems, notably for denoising the results of raytracing using a trained perceptual model or for some effects like variable radiance or transparency, and variable diffusion depending on local conditions or on the actual gameplay or during transitions and important transform of the viewed scene). So do we need a NPU? Yes, because it is generalist (but not just for AI or graphics!). They will more efficiently do something that the hardwired models used in GPU or their small number of pgrammable units will not be able to do efficiently (because these programmable units support too many instructions that are difficult to synchronize, so they are limtied by the rate of their clock as their operations are still too much sequential, even if the instruction set is much simpelr than the one used by the CPU). The GPU and NPU however does not replace the CPU which is still there and needed for controling and coordinating all works to the effective demand and to user interactions and many unpredicatable events (that can't be preprogrammed or correctly predicted and for which there's no "early sensors"). The AI concept is not the problem we fear all. The problem is how the input data is collected to train the AI model, or as input to perform the AI computation to get responses, and then where the generated response goes (who will use it, for what, and for how much time). And what will be the impact of decisions made from this uncontroled output (most often invisible to the users that cannot decipher it without designing, using and controling their own AI system trained with their own goals): data collection by "big data" third parties (too often very hostile and using very anticompetitive behaviors or using all tricks to escape the legal consequences of their abusive action) is the major problem. In itself a NPU is harmless, but the wya they are being designed and implemented is that users have no control (NPU techniologies are being integrated with networking technologies: you cannot use them with connecting these integrated NPUs to these spies that will decide which trained data model the NPU will be allowed to use, and there are hidden models preimplemented in NPUs that are designed to spy you more efficiently).
@axi0matic
I'm sure Apple's 8GB laptops will be totally 'equivalent' to 16GB for AI use...
@TheNJK57
We need a video explaining the clean shave, brutal betrayal 😂
@21Shells
Frankly I don’t want it if it means I can avoid AI features. Unfortunately it looks like the options are “run the AI locally” or “give us your data and run it in the cloud”. I don’t want either. Absolutely hate how AI is being forced into everything now.
@sownheard
The resson you want a dedicated NPU instead of just a GPU. Is because it leaves the GPU free to do its own well optimized Specialized functions. It's the same logic that got us to move on from just having a single Processing Unite
@TechAltar