Talking from mlperf: how to lead the next wave of AI accelerator

author | OneFlow Community

source | OneFlow Community

In the history of deep learning , Specialized hardware at least AlexNet and Transformers These two revolutionary moments played a key role , The papers that introduced both architectures emphasized in their abstracts GPU The role of , It also explains in detail how they parallelize the underlying computing to multiple GPU in , To speed up the calculation .

The reason to emphasize this point , Because as early as AlexNet The paper was published 20 Years ago , The deep convolution neural network applied to computer vision has been born ; Again , As early as Transformers Before publication , Applied to naturallanguageprocessing LSTMs、RNNs And self attention mechanism （self-attention mechanism） It already exists , This shows that many algorithm foundations have already existed .

However , By showing how to do large-scale calculations at a reasonable cost , The authors have made further breakthroughs in the above fields . The reason is , They got it AI The revolutionary nature of acceleration ： Expand to daily necessities （Scale-to-Commodity）.

25 Over the years , Router 、 Wired and wireless network speeds have increased 1000 times

Although improving the performance per unit cost has always been the long-term goal of computer scientists and engineers , But in some cases , Revenue will change the rules of the game , Some things that used to be considered impossible to become daily necessities will become daily necessities .

When the chip performance improves 100 times , It means that you can run a complex model in two hours , In the past, you had to spend a week , You may even need 100 Based on CPU The workstation （ Suppose you can master all these parallelization and pay for expensive electricity ）.

In the past 20 year , Due to the increase of Internet bandwidth 1000 Many times , Our lives have changed dramatically . You can imagine , Did people have to wait for hours to finish downloading their favorite TV series ？ It's so painful .

In order to maintain AI The revolutionary nature of , We need to consider which models can be commercialized and how to commercialize them .

1

AI The pitfalls of benchmarking

In recent years , We have witnessed MLPerf The rise of , This is a program aimed at AI The performance data reported by hardware and system suppliers are standardized benchmark components . It comes from 70 Many head technology companies （ Ying Wei Da 、 Google 、AMD Intel ）、 Multiple startups （SambaNova、Cerebras and Graphcore） And various universities and technical institutions （ Stanford 、 Harvard and Berkeley ） And many industry leaders .

MLPerf Benchmarks include ResNet50 and BERT Such as multiple AI application , And evaluate different sections , Including data center training 、 Data center reasoning, mobile device reasoning, etc . Every few months ,MLPerf You will receive test results submitted by major companies and institutions （ It mainly involves application running time ）, Then they will push the results to the corresponding fields . such , The submitters will be able to submit the documents according to MLPerf The competition evaluates their performance , Some potential customers can also choose ones that are more in line with their own needs AI System .

MLPerf There is an ambitious mission statement ： Based on real models 、 Open data sets and easy to understand indicators , Provide users with standardized and unbiased performance reasoning methods , To promote AI Application .

With AI Hardware is more popular , We urgently need more industry standards and indicators to understand AI The performance of the hardware . although MLPerf We have done a good job in performance evaluation , But I also want to mention three improvement measures that can expand its influence .

1、 Normalization

AI There are many kinds of accelerators , Different users （ Amateurs 、 Graduate student 、 Large enterprises, etc ） And system （ Mobile devices 、 The workstation 、 Autopilot 、HPC/ Cloud, etc. ） There are also great differences in the needs of .

Besides , There are many ways to build accelerators , Give a computational domain , You can use your imagination and creativity to design hardware and software together （ Of course , You also have to consider the market demand and cost ）. because AI Accelerators and systems are diverse , It is not easy to compare different acceleration technologies , Not to mention defining a kind of fairness 、 Unbiased accelerator comparison standard .

IEEE spectrum Once published an article entitled 《AI Training is going beyond Moore's law 》 The article （https://spectrum.ieee.org/ai-training-mlperf）, This view is based on MLPerf Published test results . Moore's law holds that , Transistor density every 18 Months will add about 2.2 times , but MLPerf The published data shows that ,AI The results of the training are 18 Improved within months 16-27 times .

therefore , Someone said right away ,“ If we are so much faster than Moore's law , So most of the performance improvement is not because we use better semiconductor technology , But because of the better acceleration method and software technology .” However , Their conclusion is not reliable . Because they don't have standardized standards to measure performance , Therefore, the contribution of hardware to performance optimization is ignored .

ResNet50 The gradual standardization of training results

Let's take a closer look at these figures . With ResNet50 Data center training for ,2018 year ,MLPerf The baseline result is to use 640 individual NVIDIA V100 GPU Achieved 6.2 minute , And in the 2021 year , Time has been reduced to 0.23 minute . In terms of numbers alone , The speed is indeed about 27 times , But this is because of the use of 3456 individual TPU v4 Accelerator , This means that the number of accelerators has increased 5.4 times . therefore , If we apply this standard to every chip , Actual performance will still improve 5 times , It's amazing .

in addition , One V100 GPU There are... On the chip 211 100 million transistors , Although Google is TPU The specification didn't reveal much , But it's with TPUv4 Competitive training chips （NVIDIA A100、SambaNova SN10、Graphcore mk2 IPU etc. ） The number of transistors is 400-600 About 100 million .

therefore , We can reasonably assume that TPUv4 With so many transistors , It can provide per second 275 Peak performance of trillions of floating-point operations , So conservative estimates TPUv4 Yes 400 100 million transistors , So we will conclude that the performance of each transistor has been improved 2.6 About times .

Last ,V100 It's using 12 nanometer CMOS Technology manufacturing , and TPUv4 Probably using 7 nanometer CMOS Technology makes （ Such as TPUv4i）.ITRS And other academic studies suggest that ,7 Nano circuits should be better than 12 Nano circuits are fast 1.5 times . therefore , Taking all these factors into account , Include more budget , Faster and better hardware capabilities , The architecture helps to improve performance by only 1.7 About times .

obviously , This is a first order approximation （first-order approximation）, Actual performance is determined by many factors other than computational semiconductor technology , For example, memory bandwidth 、 cache 、 Communication bandwidth and topology . More complex systems do need to be managed by a more sophisticated and sophisticated software stack , Increasing the number of chips or transistors does not guarantee an equal multiple improvement in performance . We must have such thinking , To better understand normalization , And performance optimization through transistors .

Why is it so urgent to optimize performance by standardizing transistors ？ The main reasons are as follows ：

1、 This is the meaning of the accelerator . If one day , Transistor technology will really stop developing as Moore's law says , In the future, we will only realize performance optimization on the premise that silicon-based technology is stagnant ;

2、 If there is no fair comparison method , Grades are just numbers , It's like a race between a cyclist and a person who drives a Lamborghini is meaningless ;

3、 The performance of a chip cannot be improved indefinitely , When the accelerator supplier develops a series of accelerators , They need to evaluate the hardware of each generation of accelerators 、 Software stack , So as to find out the real contribution that the upgrading can make to the performance optimization . meanwhile , Also need to know , Sometimes they will no longer come up with a better solution to the same problem , And we will encounter the diminishing return of chip specialization and collision “ Accelerator wall ”;

4、 Trying to invest a lot of money on an issue to improve performance is not desirable ,MLPerf Only stick to your accelerated vision , And push AI The revival has entered the first stage —— Expand to daily necessities , In order to continue to maintain their own value .

AlexNet and Transformers It opens the door to training complex models using a single system , And led a new round of breakthroughs . We need to pay more attention to standardized performance indicators , Such as unit power performance or single transistor performance . We have made some attempts in unit power performance , But such a job doesn't seem very attractive . Power measurement is troublesome , And making a breakthrough in this area will not cause a sensation like setting a new training time record .

Last , Although it's good to know the absolute performance figures , But ordinary data scientists really care about how to build a billion level system （ Have thousands of processors they can't afford ） Improve the execution time ？ This leads to our subsequent discussion .

2. Popularization

MLPerf Benchmark applications involved , Mainly 2012 to 2018 Models mentioned in academic research papers published in , and GPU As the only feasible choice for deep learning at that time , The data in those papers are models in GPU Performance results running on .

and , The characteristics of the underlying architecture involved in this paper 、 Both the super parameters and the scale of the architecture will depend on GPU To configure the , The advantages of the model will be similar to GPU Combined with the architectural advantages of , Of course , The final test results will also be affected GPU Limited by the shortcomings of .

In this way ,MLPerf The competition is actually more similar to “GPU competition ”, Also let GPU Since then, it has become AI A byword for hardware .

Don't get me wrong —— We should all attach importance to GPU, Because it is modern AI The pioneers of . without GPU Mature hardware / Software stack , Will never witness AI The rise of applications . But in addition to GPU, We now have other solutions , We should also explore the properties of native models developed on other types of architectures , They can open up some new fields .

Besides , We probably don't want a single software implementation , Or a single type of architecture , This determines the whole AI Achievements and limitations in application fields . It can be assumed , If in 2010 Years ago TPU and TensorFlow The ecological system , that MLPerf The scope of application will be different .

3. generalization

“When a measure becomes a target, it ceases to be a good measure（ When an indicator becomes a goal , It will no longer be a good indicator .）”-Charles Goodhart

MLPerf The last challenge to overcome is the lack of application generalization . About a year ago , I surveyed a leading company in the industry AI Performance evaluation of hardware suppliers . Experience tells me , It is not easy to achieve the expected performance .

So here's what happened , Even in a new Docker All the latest libraries are installed on the image , I have participated in MLPerf The running score of our model is not close to their published score . Only use the model version provided by the supplier from a dedicated Library , And after the manual configuration file overrides the compiler's decision on the model , It is possible to achieve their published scores .

Last , When I replaced the model provided by the supplier with another model （ The number of layers and size of the two models are almost identical ）, And after the same configuration , I found that the performance decreased 50% about . I heard that , Other friends have had similar experiences when using some models provided by other suppliers .

Use benchmarking “ Slow moving target ”（slow-moving targets） The above undesirable results will occur , That's not surprising .

at present ,MLPerf The benchmark has used the same set of applications for three consecutive years , Therefore, the supplier only needs to show some unrealistic things to these applications during testing “ Disposable ” performance , Can run a good result ; Most data scientists don't really understand the underlying hardware 、 Compilers and software libraries , So they don't spend weeks tuning , Not to mention creating manual configuration files that override compiler decisions .

AI One of the main challenges is to achieve “ User to hardware expression （user-to-hardware-expressiveness）”.AI The field is developing rapidly , Hundreds of new research papers are published every day , So we need to generalize the stack , So that they can “ Open the box ” Run more use cases with good performance , Instead of still focusing on the applications of a decade ago .

MLPerf We should pay more attention to , What data scientists need is a powerful acceleration stack （acceleration stack）, These acceleration stacks can take advantage of the underlying hardware to handle unprecedented workloads .

at present , majority AI Hardware suppliers give priority to customer models and requirements , Instead of MLPerf application . If MLPerf No more common 、 A more popular way , It is bound to meet more competitors , And eventually people will no longer want to see it as an industry benchmark .

2

Rethink hardware acceleration

Hardware acceleration has been the driving force AI One of the key factors for the development of the field , So understand the basic assumptions of hardware acceleration 、 Limitations of existing concepts and new development directions , For pushing AI Further development of the field is crucial .

Across the innovation gap

AI The field is research driven , Therefore, there is a close symbiotic relationship between industry and academia . Cutting edge used in production systems AI The model and its theory are published in NeurIPS、ICML、CVPR and ICLR And other papers at top academic conferences , Include AlexNet、ResNet、YOLO、Transformers、BERT、GPT-3、GANs and  Vision Transformers. Besides , Many of these papers were written by academic institutions and Google 、 facebook 、 The results of the cooperation between NVIDIA and research laboratories of Microsoft and other companies .

And AI Compared with the application field , Industry is in AI Accelerator field has been lagging behind academia . although ISCA、MICRO、HPCA and ASPLOS Dozens of new papers on accelerators will be published every year at the computer architecture summit , But the core architecture idea of accelerators deployed in production was born decades ago , Including memory processing 、 Data flow calculation 、 Pulse array 、VLIW wait .

We need to accumulate more ideas and innovations , And turn it into a new product . The adoption of new ideas is always slow , This is because it takes years to make chips , and AI There is fierce competition in the hardware field , Adopting new ideas means that it may be costly to change some of the existing functions , It's risky .

However , current AI The accelerator field has matured , Some necessary foundations have been laid , We also know how to do it effectively . therefore , It's time for suppliers to try some new ideas , Drive... Through new discoveries AI Accelerator field development . To believe in ,AI There is still room for innovation in the accelerator field ！

Give Way AI Acceleration really works on AI

modern AI Has aroused our interest , It makes us think about the infinite possibilities of the future , And put forward some ideas about philosophy 、 Sociological and ethical issues , The giants in these fields are also discussing intensively ：AI What can be done ？AI What is the future direction of development ？ however , Before we imagine , Must pay attention to AI Of “ Chicken and egg ” problem ： Hardware is the basis of the model , It's hard to imagine a new model deployed on poor performance hardware , in other words , New models require new hardware .

Due to the high cost of manufacturing new hardware , Therefore, it is necessary to have a reasonable reason to get funds to start researching new hardware . But if you don't have the hardware to run these new models in a reasonable time , It is impossible to prove the value of these new models . next , Because the actual performance of the new model was not demonstrated ,AI Innovation will be constrained by existing hardware solutions .

from AI To deep learning ： What are we accelerating ？

It is worth mentioning that , Although we call what current hardware does “AI Speed up ”, But it's actually exaggerating . in fact , It's closer to “ Deep learning speeds up ”, Or it is the product of a combination of tensor based deep learning and machine learning algorithms , It can run well on the matrix multiplication engine .

Someone might say ,“ ok , Almost all of us have heard of deep learning , It is the most valuable nowadays ‘ Killer application ’”, But that is the key ——— We mainly study deep learning , Because the algorithm library and hardware are customized for deep learning , Therefore, deep learning is the field we can explore at present , This is what you need to pay attention to when developing accelerated hardware .

We co design algorithms and hardware , It has developed from building general-purpose processors to building various architectures ; However , We optimize the data path length to a fixed size variable , Because there are fixed size algorithm engines and fixed word size memory and connections , And we try to use matrix multiplication , Because it has a simple and predictable control flow . We don't want to over optimize the hardware, resulting in a significant decline in its performance , This prevents you from exploring new features , Thus hindering the progress of the algorithm .

Although I am not a neuroscientist , But I know that the brain has many complex structures , These structures communicate in an irregular way , So we may not be able to use matrix multiplication 、 Fixed width data paths and nonlinear functions to simulate them .

therefore , We need to think , Whether this hardware evolution makes the application hardware co design space converge to local optimization ？ Maybe , If we leave the tensor intensive model, we can achieve “AI Speed up 2.0”, welcome AI The next wave of hardware .

At that time ,“AI Speed up 2.0” There is no need to multiply the matrices , But it supports irregular computing mode , And it has flexible representation and arbitrary parallel computing engine . Accelerators with these characteristics （ Or heterogeneous integration of such accelerators ） It will have a wider range of applications , It is also closer to the real AI.

To make a long story short , We need to think ： Is attention mechanism all we need ？ Convolutional neural networks （CNN） Is it the final answer ？ Whatever the answer is , Convolutional neural networks have indeed condensed most of our research results , But maybe AI There are more things that can be extended to daily necessities .

（ This document is issued after authorization , original text ：

https://medium.com/@adi.fu7/ushering-in-the-next-wave-of-ai-acceleration-a7a14a1803d1）

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