Hello my name is Bill Dally. 大家好，我是Bill Dally
I’m Chief Scientist and Senior Vice-President of Research at NVDIA Corporation. 是英伟达公司的首席科学家和高级研发副总裁
I’m gonna talk to you today about Accelerating Data Science. 今天要跟大家谈论的是如何加快发展数据科学
What we’re trying to do is take large amounts of data that we’ve collected. 我们在做的就是利用收集到的大量数据
Perhaps customer sales records, 比如说客户的销售记录
from this data glean insight. 从这些数据中获得一些信息
For example, we may wanna see if the customer purchased Item A, they may also want Item B. 举例来说，当我们看到客户购买了商品A时，我们可推测客户还可能想购买商品B
From this insight gain value. 从这一信息中，我们可获取相关价值
Perhaps greater sales by suggesting customers purchase B, if they purchased A. 我们可进一步推荐购买了A产品的客户购买B，从而增加销售额
A lot of modern science is based on the technology of machine learning of artificial intelligence. 许多现代科学都基于人工智能的机器学习技术
There’s been a revolution in machine learning in the past decade. 在过去十年中，机器学习经历了革命性的变化
This revolution has really been spurred by hardware. 这些革命性的变化实际上是由硬件推动的
At the core of machine learning is deep neural networks. 机器学习的核心是深度神经网络
Deep neural networks have been around since at least the 1980s — 深度神经网络最早出现在20世纪80年代—
actually the early work on them goes back to the 1950s and 60s. 而关于他们的早期研究则可追溯至五六十年代
The algorithms, the methods, all these things were around for at least 20 years. 相关的算法和方法等实际上已经存在了至少20年之久
The other two ingredients of deep learning — 深度学习的另外两个要素—
think of the network down here as being the algorithms — 将这里的网络看作算法—
is lots of data. 而另一要素则是大量的数据
There was lots of data around for at least a decade before things really began taking off 深度学习兴起前，这些大量的数据已经存在了至少有十年之久
but the missing ingredient between algorithms 但是在算法
and data and hardware was the hardware. 数据和硬件三大要素中，我们缺少的要素则是硬件
It wasn’t until the last decade that we really had computing 直到最近十年，我们才真正拥有一定的计算能力
that was fast enough to train neural network models 可快速训练大型模型
at scale with large amounts of data 处理大量数据
to achieve performance that was better than other methods. 从而获得比其他方法更好的性能
For Computer Vision this took place with AlexNet — illustrated here — in 2012. 计算机视觉的兴起 始于2012年AlexNet（如图所示）问世
And it really was the spark that ignited the current revolution in deep learning. 这确实是点燃当前这场深度学习革命的火花
Now once we achieved better performance than previous methods, 现在，我们获得了比之前方法更好的性能
and AlexNet in one year had a greater improvement on the ImageNet benchmark AlexNet在短短一年内大大改进了ImageNet基准
than conventional methods had in the previous 5 years, 优于过去5年的常规方法
once we achieved these tremendous results, the race was on to do better, 一旦我们取得了这些巨大成就，接下来就会做得更好
and doing better requires even better hardware. 而做得更好就需要更好的硬件
This chart shows over time — 这张图展示了深度学习是如何随着时间变化的
starting with AlexNet in 2012 — 从2012年AlexNet出现开始
shows we’ve gone to better models for vision like ResNet 我们已经使用了更好的视觉模型，例如ResNet
and models for natural language processing like BERT 和自然语言处理模型，例如BERT
and even larger transfer models like Megatron-BERT. 以及更大的传输模型，例如Megatron-BERT
There’s been 5 orders of magnitude increase in the amount of computing performance 按照pfs-day来衡量算力，训练这些网络所需的
needed to train these networks in terms of Petaflops days. 计算性能提高了5个数量级
So to get better progress in deep learning to get better progress in data science 因此，要在深度学习、数据科学方面取得更大的进展
we need to constantly get faster hardware 我们需要不断地获得运行更快的硬件
because that enables bigger models to train on larger amounts of data, 这样才能训练更大的模型来处理大量数据
to get better insights, to deliver better value. 以获得更好的信息，提供更高的价值
But there’s a problem, 但有一个问题
we’re trying to deliver better computing performance in an era where Moore’s Law is dead. 我们正在努力提供更好的计算性能，但摩尔定律已经失效。
Historically, since the mid-1980s through about the year 2005, roughly a 20-year period, 从历史上看，从20世纪80年代中期到大约2005年，在这20年左右的时间内
computing performance increased at 50% per year. 计算性能以每年50%的速度增长
Largely through improvements in processing technology being converted to instructional parallelism. 这主要是通过处理器技术向指令级并行转换进步实现的
However, since 2005 但自2005年以来
the number of factors have conspired to end that scaling 很多因素共同作用，结束了这种快速增长
and currently CPU performance is roughly flat 当前的CPU性能大致保持平稳
increasing only 3% per year 每年仅增长3％
as illustrated in this chart from the textbook written by two recent Turing Award winners, 如这张图表所示，该图引自最近获得图灵奖的
John Hennessy and Dave Patterson. John Hennessy和Dave Patterson所撰写的教科书
So when we continue to scale computing performance 因此，当我们不断提升计算性能，
to enable deep learning and hence data science, 以支持深度学习并进而实现数据科学时
is to switch from trying to depend on process technology to innovations in architecture 就是从尝试依赖处理器技术转向架构创新
and the way we do that is thru GPUs — graphics processing units. 要实现这种转变，我们通过GPU，也就是图形处理器
Our current GPU is the Ampere A100 — 我们当前所用GPU是安培A100
just announced this past May — and its truly an amazing chip. 今年五月刚刚发布—这是一款非常厉害的芯片
It’s a 7-nm chip that’s 54 billion transistors. 这是一款7纳米芯片，包含540亿个晶体管.
It incorporates a number of new technologies 芯片采用了许多新技术
and this is the innovation that’s required 这才是我们现在所需要的创新
now that we’re really not getting improvements 因为我们现在并没有通过处理器技术获得进步
thru process technology to continue scaling deep learning. 去持续扩展深度学习
It has a 3rd-generation Tensor Cores 该芯片采用了第三代Tensor Core
which have 20 times the performance on TF32 as our previous cores. 其性能是我们以前内核TF32的20倍
They also support new sparsity. 这一芯片还增加了新的稀疏特性
It turns out that neural networks, like the networks in our own brains, are sparse. 事实证明，神经网络就像我们人类大脑中的网络一样具有稀疏性
You don’t need a connection from every neuron to every other neuron 我们并不需要每个神经元都相互连接
but computers like to do things that are very regular and hence dense. 而计算机喜欢做非常有规律、加重密集连接的事情
In Ampere we’ve come up with a compromise between the two. 安培A100是两者之间的折衷方案
We exploit structured sparsity 我们能够通过处理每四个权重中
by being able to handle patterns where two out of every four [weights] are non-zero. 有两个非零权重的模式来开发结构化稀疏性
That essentially doubles our performance on many neural networks. 这实际上使我们在许多神经网络上的性能提高了一倍
Ampere A100 using a technology called MIG (Multi-instance GPU) 安培A100通过使用MIG (多实例GPU) 技术
can be divided. 从而实现资源切割
We have many smaller problems. 我们要同时处理许多小问题
You can take a single A100 那您可以购买一台A100
and use it virtualized to solve many people’s problems simultaneously 并对其进行虚拟化处理，以同时解决许多人的问题
and to scale up we have a 3rd-generation of our interconnect and link NV can then be switched. 在升级方面，我们有第三代互联，可以切换链路NV
So many A100s can be combined aided thru EDX box and larger systems beyond that box. 因此，可以通过EDX箱，甚至比EDX箱更大的系统，把多个A100组合起来
Here’s some specs on A100. 这里有A100的一些规格
I won’t go into the details 在此我就不细说了
but the important thing is that we’ve introduced a new floating point for it, 但是重要的是我们为此引入了一个新的浮点
a TF32 that has 156 teraFLOPS of a format that has basically the range of FP32 即TF32，其性能达到156TFLOPS，基本上包含了FP32的范围
and the precision of FP16 和FP16的精度水平
and if you use that in corresponding with our sparsity 如果您配合使用我们的稀疏性
you get the effect of 312 teraFLOPS of performance 您将获得312TFLOPS的性能效果
that’s added with a range and precision adequate to train most neural networks. 并具有足以训练大多数神经网络的范围和精度
This is a real game changer 这彻底颠覆了以往传统的模式
that’s 20 times the training performance of our previous generation. 其训练性能是上一代的20倍
For inference it has over 600 teraFLOPS innate performance 就推理性能而言，它的内在性能超过600 TFLOPS
you get if you exploit the 2 out of 4 sparsity 如果你利用2/4稀疏性
that’s over 1200 teraFLOPS of effective performance for inference. 其有效性能就会超过1200 TFLOPS
The structured sparsity really brings a lot of additional speed to neural networks. 结构化稀疏性确实大大提高了处理神经网络的速度
If you take a dense matrix, 如果您使用密集矩阵
you can always prune it down to 则可以随时对其进行剪枝
get at least 2X sparsity expedited back in 2015 fully connected network 得到至少2倍的稀疏性，加速到2015年完全联通的速度
so that you would be able to prune 90% of the weight 因此你通常能够剪掉90%的权重
and getting down to 10% of the weights remaining 剩余10％的权重
and for convolutional networks that’s pruning out 70% leaving 30% of the weights. 而对于卷积网络而言，它要剪掉70％的权重，剩下30％的权重
So 2X is very conservative 因此快2倍的说法是非常保守的
and if you do you this by executing only for the remaining weights, 如果只执行剩余权重
you’re able to get 2 times faster execution. 则可将执行速度提高2倍
and even with the various overheads 且即使有各种开销
that don’t get accelerated by this 2X 也没有达到2倍的速度
On real natural language fussing models like BERT-Large, we’re able to get a 50% improvement in performance. 在像BERT-Large这样真正的自然语言处理模型中，我们可将性能提高50％
Many people say that neural networks particularly inference 很多人说神经网络，特别是在“推理”方面
are going to move over to dedicated accelerators 会逐渐改用专用加速器
but we don’t see this happening. 但我们看到情况并非如此
The reason for this is that by building specialized instructions 这样做的原因是，通过构建
like the matrix multiplied accumulate instructions that are executed by our Tensor Cores, Tensor Core由执行的矩阵乘累加指令之类的专用指令
we’re able to realize almost all the benefits of a dedicated accelerator 我们几乎能实现专用加速器的所有功能
and at the same time retain the generality of the programmable engine. 并同时保留可编程引擎的通用性
What this chart illustrates 这张图说明的是
is that back in our Kepler generation 在开普勒时代
when the largest operation we had to build neural networks 我们建立神经网络所需的最大操作
was a half position footing, multiply, accumulate, 是半位求和、乘积和累加
the overhead programmability dominated the energy of that instruction. 开销可编程性支配了该指令的能量
When we moved to doing dot products 当我们做向量点积时
we’re doing 4 times as much work with the same overheads. 在相同的额外开销下完成的工作量是原来的4倍
The overhead drops by a factor of 4. 因此额外开销变成了四分之一
When we move to doing half precision matrix multiply accumulate, 当我们做半精度矩阵乘法时
we’re now doing 64 operations in a single instruction. 我们现在可以在一条指令中执行64个操作
So we’re amortizing a lot more energy going into payload over 因此，我们要摊销更多的能量
which the same overhead is being amortized. 以分摊相同的开销
Now we’re not just 20% overhead 现在我们不仅有20％的开销
and with the integer matrix multiply accumulate 而且与整数矩阵相乘时
only 16% of the energy is going to overhead. 累计只有16%的额外开销
What this says is that the cost programmability is small. 这就是说，成本可编程性很小
For half precision 22% for integer 8, up 16%. 对于整数8来说，半精度为22％，上升了16%
So in the arithmetic part of the computation, 因此在计算中的算术部分
the best a dedicated accelerator can do by eliminating 通过消除这种可编程性开销
this overhead of programmability is 16% better 专用加速器可以做到的最好结果是提高16％
and that 16% is a very small cost to pay to be able to code up the latest algorithms 这16％是能够编码最新算法的超低成本
and not tie your fate to whatever happens to be the trend at the time 不要将命运与当时的趋势联系起来
the accelerator’s architecture which may be many years before it’s shipped. 因为加速器的结构可能多年之后才发布
So basically, by building these special instructions like having our Tensor Cores, 因此，基本上通过创建这些特殊指令（例如使用我们的Tensor Core）
we’re able to match the performance of deporting accelerators 我们能够实现专用加速器的性能
and still retain the programmability. 并仍保留其可编程性
This chart shows how innovation has enabled us to scale 此图显示了创新如何使我们能够发展
even the Moore’s Law is dead. 即使摩尔定律已无效
It shows single-chip inference performance over the last 8 years, 它显示了过去8年的单芯片推理性能
starting with the introduction of our Kepler generation in 2012, 从2012年推出开普勒一代开始
this is the GPU that was used with AlexNet 这是与AlexNet一起使用的GPU
and quite frankly it was the GPU that was designed for graphics and high-performance computing 坦率地说，这是专为图形和高性能计算而设计的GPU
not really tailored for deep learning. 并非真正为深度学习量身定制的
Once we started getting involved in deep learning 一旦我们开始参与深度学习
which actually happened a couple of years before it could influence the architecture of Kepler 实际上深度学习在影响开普勒的体系结构之前几年就已经出现了
we started adding features to our GPUs for deep learning. 我们便开始在GPU中添加用于深度学习的功能
There’s a small increase with our Maxwell generation. 在麦克斯韦这一代中有少量添加
In Pascal we had the dot product instruction 在帕斯卡一代中，我们增加了点积指令
which gave us a boost. 这给了我们一大推力
In Volta we added the Tensor Cores with the HMPH MA instruction. 在伏特一代中，我们在HMPH MA 指令中添加了TensorCore
In the Turing generation 在图灵一代
we added support Q800 in the Tensor Cores, 我们在Tensor Core中增加了Q800支持
and in our Ampere generation 在安培一代
we now have better Tensor Cores with the Tensor 32 format 我们有了更好的具有Tensor 32格式的Tensor Cores
as well as FP16 into 8 million FP16达到800万
and support for sparsity which gives us if we’re recording the sparsity, 它具有稀疏性（如果我们有记录稀疏性）
an equivalent of over 1248 per second. 峰值算力可以达到1248TOPS
So you can see that despite the fact Moore’s Law’s dead 因此，你可以看到尽管摩尔定律已无效
CPU performance isn’t scaling, CPU性能也没有快速增长
we’re getting large increases through our innovations, through architecture 我们通过创新、架构
and some extent circuit design. 以及电路设计同样实现了性能大幅提高
And it shows that a lot of these innovations are independent of process technology. 这也表明，这些创新很多都是独立的处理器技术
Nodes here are color-coded by process technology Kepler and Maxwell, 这里的节点由开普勒一代和麦克斯韦一代工处理器技术进行颜色编码
we’re in 28 nanometers Pascal V100 我们使用的是28纳米的Pascal V100
and maturing architectures were all in variance of 16 nanometer technology 而成熟的架构都采用16纳米技术
and then Amperes are for 7 nanometer chips 然后安培一代适用于7纳米芯片
but most of these jumps here are not due to process technology, 但这里的大多数跃迁并非归因于处理器技术
they’re due to innovations in architecture — 而是来自于架构上的创新
things like tensor cores, 比如Tensor Cores
things like support for structured sparsity. 比如结构化稀疏特性
Now this is all really important 现在这一切真的很重要
because it enables applications 因为它可以实现应用
and deep learning applications have changed a lot 深度学习自
since the days of AlexNet when everyone was focused on images. AlexNet时代图象研究热潮以来，已发生了巨大的变化
One of the most exciting applications today is multi model conversational systems 其中最最令人振奋的应用程序是多模式会话系统
where we would have inputs and outputs that are both audio in nature and visual in nature. 在该系统中，我们将获得自然的音频和视频的输入和输出
We may for example have a customer ask a question 例如，当一名顾客问问题
and at the same time be pointing to a product in the store. 同时指着商店里的产品时
So, we want to be able to look at the video, 我们希望能够观看视频
understand where their pointing, 了解他们的指向
listen to the audio, understand their question. 听音频，了解他们的问题
Use some natural language processing in a question answering system 在问答系统中使用一些自然语言处理
to handle this and this goes thru many aspects of deep learning. 来处理此问题，这涉及到深度学习的许多方面
One network for the denoising. 一个用于降噪
Another one to convert audio to text 一个用于将音频转换为文本
and another one to take that text and answer questions about it. 还有一个提取文本、回答问题
Similarly, we want to combine that with information from the video model. 同样，我们希望将其与视频模型中的信息结合起来
Perhaps we want a recommendation system 我们可以有一个推荐系统
to recommend which product they might like 基于顾客的喜好
given their stated desires and constraints that they’re trying to meet, 来推荐顾客可能想要的产品
and then we want to synthesize that into text to speech 然后我们想将其合成为文本再做成语音
and then coding that into a wave form to speak to them 然后将其编码为波形以与他们交谈
and perhaps give them an illustration of what a recommendation system is. 也许可以给他们讲解我们的推荐系统
And in NIVIDIA we have a system called Jarvis 在英伟达，我们有一个名为Jarvis的系统
which combines all these various pieces and allows you to do that. 该系统将所有这些不同部分组合在一起，可以实现这一点
Another really important part of Data Science is used for building recommenders. 数据科学的另一个非常重要的部分用于构建推荐系统
Basically, taking lots of data about what people like. 基本上，它会收集有关人们喜好的大量数据
Given that if they buy Item A, they also buy Item B. 假设他们购买了商品A，则可能会购买商品B
Or if they watch movie A, they might watch movie B. 或者如果他们观看了电影A，则可能会观看电影B
We wanna take lots of those recommendations 我们想采纳很多这类建议
and build a model that includes imbedding the items, 并建立一个包含嵌入项目的模型
because they’ve extremely sparse space here. 因为这里的空间稀疏性很高
Perhaps tens of thousands, or hundreds of thousands of items. 也许数万或数十万个项目
We don’t wanna have a one-hubvector that’s a hundred thousand-wide. 我们不想有一个十万宽的单中心矢量
We embed this into a shorter vector 我们将其嵌入较短的向量中
and then out of this we feed it thru a network that generates the recommendations 然后通过生成建议的网络将其馈入
and there are actually many types of neural networks in here. 实际上这里存在许多类型的神经网络
In NVIDIA 在英伟达
we actually have a product called Merlin 我们有一个名为Merlin的产品
which enables people to very quickly build these large recommenders 它能实现快速构建这些大型推荐系统
by allowing you to build these embedding tables with NVTabular 方法是通过构建表格形式的嵌入表
and to build a recommender system with HugeCTR. 并构建HugeCTR推荐系统
And you can see the difference over on the left 在左侧，您可以看到
here of you using GPUs which have been able to scale for architecture innovations. 在利用CPU和GPU拓展架构创新时的差异性
On a CPU this would take hours for the extract, translate, 在CPU上，整个流程中，提取、转换
load (ETL) part of the process 以及加载环节（ETL）就需要数小时的时间
and days for training. 而训练则需要数天
Here it takes 3 minutes to do the input part ETL 在这里， ETL环节需要3分钟
and the training takes just 16 minutes. 而训练只需16分钟
And the actually raises an important point, 这就提出了一个重要观点
to accelerate data science 要加速数据科学
we don’t just need to accelerate the execution of the deep learning models 我们需要的不仅仅是加速深度学习模型的执行
but the training part and the inference part, 还应包括训练和推理部分
we also have to accelerate the entire pipeline 我们还必须加速整个流程
including the extract, translate, and load part of the pipeline. 包括提取、转换和加载
This chart shows how that’s changed over the years. 此图表显示了多年来的变化
Originally people did a lot of the data science. 最初，人们在数据科学领域做了很多努力
And in Hadoop we were doing the operation went thru disks while doing the basic match operations. 在Hadoop中进行基本匹配操作时，我们通过磁盘进行操作
When they moved to Spark they essentially did the same operation 当转到Spark时，他们基本上也是执行相同的操作
but they were doing it now in memory. 但现在他们在内存中执行此操作
We can move Spark to GPUs and get better performance. 我们可以将Spark移至GPU并获得更佳性能
We’ve recently introduced a software package called Rapids 我们最近推出了一款称为Rapids的软件包
that does even better than that 它在这方面表现更好
by accelerating the entire pipeline, the loading of the data, 通过加速整个流程、数据加载
the ETL and the training as well as the imprints, ETL训练以及影响
and so we’re able to get 50 to a 100 times improvement over running traditional Spark on CPUs. 因此，与在CPU上运行传统Spark相比，我们可以提高50到100倍。
This is illustrated by the TPCS-BB benchmark which is very illustrative of this. TPCS-BB基准测试可以说明这一点
Running at a nominal size, 以标称尺寸运行
we’re able to get 51 times speed up and greater than 40 times 与CPU相比，我们可以提高51倍的速度
savings in cost compared to a CPU. 并节省40倍以上的成本
So let me talk briefly about the road ahead. 因此，我想简要地谈谈未来的发展方向
We have to constantly innovate to try, to keep performance scale 我们必须不断创新，努力提升性能
to enable ever larger neuron network models to be trained on ever larger data sets 实现在更大的数据集上训练更大的神经元网络模型
so that we can continue to enable progress in data science. 从而不断推动数据科学的进步
We’re delivering today with our Ampere GPU 我们今天推出的是安培GPU
over 3 TeraF1OPS per watt inference performance. 其推理性能超过3 TeraFlOPS
We wanna do a lot better 我们想做得更好
and in NIVIDIA research we’ve been focusing on this. 事实上，在英伟达的研究工作中，我们也一直专注于此
We’ve published a number of papers about what we call our MAGNet project, 我们发表了许多关于我们MAGNet项目的论文
which is actually a design exploration tool 这实际上是一个设计探索工具
which lets us generate many different flavors of deep learning accelerators 它使我们能够生成许多不同类型的深度学习加速器
and understand the trade-offs between energy and performance and flexibility. 并了解能耗、性能和灵活性之间的平衡
By doing this 通过这样做
we are able to explore the design space very efficiently 我们可以非常有效地探索设计空间
but what we find is that a lot of the energy is donated by memory and data movement. 但是我们发现，内存和数据移动占用了大量能量
This chart shows over a number of different cases and scaling with voltage. 这个图显示了多种不同情况下随电压的变化情况
How that changes? 具体的变化情况是怎样
And that on the right here which is the most efficient data point running at relatively low voltage of .4 volts. 右侧是在0.4伏的较低电压下运行的最有效的数据点
The data path doing the arithmetic is down in the blue here 进行算法运算的数据路径在此处占在
at the bottom of the relatively small fraction of the overall energy. 总能量的相对较小部分的底部呈蓝色
The bulk of the energy is in accessing memories for weights 大部分能量都在访问存储器中以进行权重
like light green activations and darker green and doing accumulation based at the output activation memory access. 如浅绿色激活和深绿色激活，并基于输出激活存储器访问进行累加
So the energy is really dominated by memory and data movement. 因此，能量实际上是由内存和数据移动支配的
By optimizing that we can do a lot better. 通过优化，我们可以做得更好
In this particular data point we were able to achieve 29 TOPS/W 在这个特定的数据点上，我们能够达到29 TOPS / W
that’s another order of magnitude better than Ampere. 比安培一代高出另一个数量级
We have a lot of projects underway using different data formats: 我们正在进行许多使用不同数据格式的项目
cover data representation mode, loads of quantization, pruning techniques and the like. 包括数据表示模式、量化负载、简化技术等
We expect to do even better. 我们希望能做得更好
We’ve set an informal goal for ourselves 我们已为自己设定了一个非正式目标
outfitting a hundred TOPS/W over the next few years. 在接下来的几年中要[配置] 100 TOPS / W的性能
There’s a lot of headroom ahead to enable even better data sites. 要实现更好的数据站点，还有很多空间
So let me wrap up. 我简要总结一下
Deep learning is really fueling a revolution in AI and data science 深度学习确实在推动AI和数据科学领域的一场革命
because we’re able to take information now 因为我们现在能够获取信息
and using neural networks extract insight from that information 并通过神经网络用比以前
in much more powerful ways than was previously possible 更强大的方式从信息中获取信息
and this is across many different application areas. 并且涉及许多不同的应用领域
This revolution has been enabled by hardware. 硬件使这场革命成为可能
There are only 3 ingredients to deep learning: it’s algorithms, data and hardware. 深度学习只有3种要素：算法、数据和硬件
The algorithm’s been around since the 1980s. 算法自1980年代开始发展
The data in the volumes necessary to do deep learning 在这个深度学习领域取得成功之前
have been around for at least a decade before this really took off. 发展深度学习所需的数据量至少已经存在了十年
Hardware was the spark in a quicker GPUs 硬件是更快的GPU中的火花
that ignited the flame that’s fueling the revolution in deep learning today 点燃了火焰，助力当今深度学习的革命发展
and it’s also limiting the rate at which we can scale deep learning. 但是这也限制了深度学习的发展速度
Our progress is limited by our ability to build hardware 因为硬件的能力限制了我们的发展
that is fast enough to train these larger models and larger data sets in reasonable amounts of time. 我们所需硬件的运行速度必须在合理的时间内训练这些较大的模型和数据集
So efficient hardware enables new capabilities. 因此高效的硬件可实现新功能
Today we’re able to do this with our recent Ampere architecture 今天，我们能够使用最新的安培架构来做到这一点
which is 1.2 TOPS int8* and over 3.1 TOPS/W and A100上的HTM内存的速度为1.2 TOPS int8*，超过3.1 TOPS / W
1.5 terabytes per second of memory bandwidth with the HTM memory on the A100. 每秒的存储带宽为1.5 TB
And this basically enables us to do the largest models people can envision today 这基本上能使我们支持人们今天所设想的最大模型
and even some larger ones. 甚至更大的模型
It pretty gonna enable us to do a lot of very demanding applications 它甚至使我们能够执行许多高要求的应用程序
like conversational AI, 例如对话式AI
where we can have multi-modal systems 我们可以拥有多模式系统
where customers ask questions of our system 客户可以在其中询问系统问题
and we provide answers to them in a natural format. 然后我们自然地提供答案
On the recommender systems 在推荐系统上
where we can basically take large amounts of data 我们基本上可以获取大量数据
and use it to recommend patterns that will be relevant to various people. 并使用它针对各种人群推荐相关模式
We want to continue to enable this to go even further 我们希望继续发展使它走得更远
to build larger models and more powerful applications 以构建更大的模型和更强大的应用程序
to extract more insight and more value from the data we have 以从我们拥有的数据以及将要收集的大量数据中
and from larger amounts of data we will be collecting. 提取更多的信息和价值
And we see a path to continue to scale this performance even without any advances in process technology 我们发现了一条即使处理器技术没有任何提高也能提升性能的路径
by doing a deep study of where the energy goes in these deep learning systems, 方法就是深入研究这些深度学习系统中能量流向
and being able to tailor number representations 并能够定制数字呈现
and structure the architecture and the memory system 构造架构和内存系统
because most of the energy goes into data movement 因为大部分能量都花在了数据传输
and accessing memory not into actually doing the arithmetic. 和访问内存上，而不是实际运算
We see a path forward for 我们认为
at least seven orders of magnitude more improvement in performance per watt 每瓦性能至少要提高七个数量级
an increase in performance. 性能就会有所提高
Thank you very much for your time. 非常感谢您的参与
I’m sorry I cannot be there in person 同时也非常抱歉我未能亲临现场与大家见面
but I hope you have a great day. 祝大家度过美好的一天
Bye. 再会