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stat Analytica 2021-08-14

We'll talk about statistical inference in this section.

Statistics is one of the fields of mathematics that deals with data collection, analysis, interpretation, and visualization of numerical data.

We will go over the definition of inference, kinds of inference, solutions, and examples of inferential statistics in depth in this post.What exactly do you mean when you say statistical inference?Statistical inference is a strategy for analyzing data and drawing inferences from it in order to account for random variations.

The basic goal of statistical inference is to forecast the sample's uncertainty or variance from sample to sample.

This provides a selection of real-value options for the specified population samples.

To give correct findings that are needed to evaluate the results of research activity, a thorough study of the data is required.

collect
0
Kay Pry 2018-11-28

Amazon Web Services today announced Amazon Elastic Inference, a new service that lets customers attach GPU-powered inference acceleration to any Amazon EC2 instance and reduces deep learning costs by up to 75 percent.

“What we see typically is that the average utilization of these P3 instances GPUs are about 10 to 30 percent, which is pretty wasteful with elastic inference.

You don’t have to waste all that costs and all that GPU,” AWS chief executive Andy Jassy said on stage at the AWS re:Invent conference earlier today.

“[Amazon Elastic Inference] is a pretty significant game changer in being able to run inference much more cost-effectively.”

Amazon Elastic Inference will also be available for Amazon SageMaker notebook instances and endpoints, “bringing acceleration to built-in algorithms and to deep learning environments” the company wrote in a blog post.

It will support machine learning frameworks TensorFlow, Apache MXNet and ONNX.

collect
0
James Woodson 2021-03-19
img
A Xiaomi survey asks questions about every aspect of modern smartphones - but there's no mention of telephoto cameras.
collect
0
Michel Smith 2021-11-02
img

By explicitly describing any implicit linkages and connections between your data sources, you may construct a more complete and accurate representation of your data.

This includes the capability to represent various definitions for the same data, enabling stakeholder engagement, and conveying situational or theoretical facts.The procedure Inference Engine for Smart Data is the next level, which is referred to as Big Data.

Specifically, it suggests the targeted and quality treatment of a big volume of data gathered in your organization, by breaking it down into small batches with the goal of obtaining actual value for your business through data and applying it to a specified objective.Capture business and domain rules in the data model; the Inference Engine implements these rules intelligently during query execution.

Oftentimes, organizations already have data models stored in database schemas, data dictionaries, or Excel files.The gathering of data without filtering is one of marketers', pricing managers', and internet business strategists' major challenges.

And it is for this reason that having tools you can rely on to choose, process, and analyses data in packs becomes more critical than ever to maintain focus.The gathering of data without filtering is one of marketers', pricing managers', and internet business strategists' major challenges.

And it is for this reason that having tools you can rely on to choose, process, and analyses data in packs becomes more critical than ever to maintain focus.Only by monitoring appropriate competition will your firm be able to avoid falling for deceptive signs about direct effect strategy.

collect
0
David Clary 2019-06-24
img

A major consortium of AI community stakeholders today introduced MLPerf Inference v0.5, the group’s first suite for measurement of AI system power efficiency and performance.

Inference benchmarks are essential to understanding just how much time and power is required to deploy a neural network for common tasks like computer vision that predicts the contents of an image.

The suite consists of 5 benchmarks that include English-German machine translations with the WMT English-German data set, 2 object detection benchmarks with the COCO data set, and 2 image classification benchmarks with the ImageNet data set.

Submissions will be reviewed in September and MLPerf will share performance results in October, an organization spokesperson told VentureBeat in an email.

The inference standards were decided upon over the course of the past 11 months by partner organizations such as Arm, Facebook, Google, General Motors, Nvidia, and Toronto University, MLPerf said in a statement shared with VentureBeat.

MLPerf Inference Working Group cochair David Kanter told VentureBeat in a phone interview that benchmarks are important to using inference systems to definitively decide which solutions are worth the investment.

collect
0
Joe Richards 2018-09-12
img

Nvidia today debuted the Tesla T4 graphics processing unit (GPU) chip to speed up inference from deep learning systems in datacenters.

The T4 GPU packed with 2,560 CUDA cores, and 320 Tensor cores with the power to process queries nearly 40 times faster than a CPU.

Inference is the process of deploying trained AI models to power the intelligence imbued in services like visual search engines, video analysis tools, or when you ask an AI assistant like Alexa or Siri a question.

As part of its push to capture the deep learning market, two years ago, Nvidia debuted its Tesla P4 chip made especially for the deployment of AI models two years ago.

The T4 is more than 5 times faster than its predecessor the P4 at speech recognition inference and nearly 3 times faster at video inference.

Analysis by Nvidia found that nearly half of all inference performed with the P4 in the span of the past two years was related to videos, followed by speech processing, search, and natural language and image processing.

collect
0
Joseph Cormier 2017-06-22
img

What are the chances that he has the disease?

We don't actually need to know the size of the population but it makes it easy to show you some numbers, so we'll assume a total population of two thousand; so we have 1,000 infected and 1,000 healthy.

We can summarise the figures in this table:

Let's consider simply those who, like Bob, tested positive.

So, on this showing, it looks as if the intuitive answer is correct.

Below, are those results – summarised:

collect
0
Christopher Driskell 2019-11-06
img

Today marks the release of the first results from the MLPerf Inference benchmark, which audits the performance of 594 variations of machine learning acceleration across a variety of natural language and computer vision tasks.

The benchmark is intended to create an industrywide standard for judging inference performance.

Each system takes a unique approach to inference and presents a trade-off between latency, throughput, power, and model quality, according to a white paper from organizers of the benchmark.

“The final results show a four-orders-of-magnitude performance variation ranging from embedded devices and smartphones to data-center systems,” the paper reads.

The analysis of CPUs, GPUs, and TPUs in datacenters and edge devices is the product of more than 30 organizations and 200 machine learning engineers and practitioners from Alibaba, Facebook, Google, Intel, Microsoft, Qualcomm, and Samsung.

The first MLPerf inference measured the performance of machine learning deployment tech from 14 organizations, representing 44 systems in total.

collect
0
Peter Williams 2019-10-15
img

Causal inference is a new language within machine learning used to help teams better understand causes and impacts so they can make better decisions.

Causal inference is only now starting to move outside the world of academics and research scientists — becoming a more relevant asset for businesses.

We’re exploring how various elements of machine learning can help break through confusing, or even conflicting, observational data and give insights that drive businesses forward.

In my experience, I see two key scenarios where companies can make use of causal inference — the planning side and the impact assessment side.

Most companies have high-level objectives like growing the user base, reducing customer churn, or increasing conversions.

It helps you zero in on the most important areas so you focus your efforts in the right place.

collect
0
Rosalie Lee 2019-11-07
img

“If all 7.7 billion people on Earth uploaded a single photo, you could classify [them all] in under 2.5 hours for less than $600”

Google and Nvidia have both declared victory for their hardware in a fresh round of “MLPerf” AI inference benchmarking tests: Google for its custom Tensor Processing Unit (TPU) silicon, and Nvidia for its Turing GPUs.

As always with the MLPerf results it’s challenging to declare an overall AI leader without comparing apples with oranges: Alibaba Cloud also performed blisteringly strongly in offline image classification.

These performance improvements are rapidly filtering down to the enterprise level: as sophisticated customer service chatbots, models to predict investment outcomes, to underpin nuclear safety, or discover new cures for disease.

New Smartphone App for AI Inference Benchmarks

The MLPerf Inference v0.5 tests contain five benchmarks that focus on three machine learning tasks: object detection, machine translation, and image classification.

collect
0
Joshua Herbert 2019-11-07
img

Machine-learning expert David Kanter, along with scientists and engineers from organizations such as Google, Intel, and Microsoft, aims to answer that question with MLPerf, a machine-learning benchmark suite.

Measuring the speed of machine-learning platforms is a problem that becomes more complex the longer you examine it, since both problem sets and architectures vary widely across the field of machine learning—and in addition to performance, the inference side of MLPerf must also measure accuracy.

As an example, Google trained Gmail's SmartReply feature on 238,000,000 sample emails, and Google Translate trained on trillions of samples.

If you're heavier on old-school computer science than you are on machine learning, this can be thought of as similar to the relationship between building a b-tree or other efficient index out of unstructured data and then finding the results you want from the completed index.

Performance certainly still matters when running inference workloads, but the metrics—and the architecture—are different.

The same neural network might be trained on massive supercomputers while performing inference later on budget smartphones.

collect
0
Donald Broussard 2019-04-30
img

“Thrilling” to wield this kind of inference firepower

Google Cloud says it has made NVIDIA T4 GPUs cloud instances available in eight regions, improving the ability of customers to run demanding AI inference workloads.

NVIDIA T4s, first released in October last year, are a high-end, single-slot, 6.6-inch PCI Express Gen3 Deep Learning accelerator based on the TU104 NVIDIA GPU.

They ship with 16 GB GDDR6 memory and a 70W maximum power limit and are offered as a passively cooled board that requires system air flow to operate the card.

Google’s announcement is good news for NVIDIA, which had noted “dramatic” pause in hardware spending by hyperscale cloud providers in its last earnings.

Chris Kleban, Google Cloud’s GPU product manager, wrote late Tuesday: “NVIDIA’s T4 GPU… accelerates a variety of cloud workloads, including high performance computing (HPC), machine learning training and inference, data analytics, and graphics.”

collect
0
John Ruybal 2018-09-19
img

(Reuters) — Alibaba will set up a dedicated chip subsidiary and aims to launch its first self-developed AI inference chip in the second half of 2019 that could be used for autonomous driving, smart cities and logistics.

The Chinese firm said at an event in Hangzhou on Wednesday that the new subsidiary would make customised AI chips and embedded processors to support the firm’s push into fast-growing cloud and internet of things (IoT) businesses.

Alibaba’s aggressive drive to develop its own semiconductors comes as China’s government looks to raise the quality of home-made chips to help propel high-tech domestic industries from cutting-edge transport to AI healthcare systems.

In April, Alibaba bought a Chinese microchip maker Hangzhou C-SKY Microsystems to help bolster its cloud-based “internet of things” (IoT) business.

Jack Ma, Alibaba co-founder and chairman, said then that China needed to control its “core technology” like chips to avoid over-reliance on U.S. imports, something which has been put in the spotlight by whipsawing trade tensions.

collect
0
Wayne Konwinski 2018-11-28
img

Amazon today announced Inferentia, a chip designed by AWS especially for the deployment of large AI models with GPUs, that’s due out next year.

Inferentia will work with major frameworks like TensorFlow and PyTorch and is compatible with EC2 instance types and Amazon’s machine learning service SageMaker.

“You’ll be able to have on each of those chips hundreds of TOPS; you can band them together to get thousands of TOPS if you want,” AWS CEO Andy Jassy said onstage today at the annual re:Invent conference.

Inferentia will also work with Elastic Inference, a way to accelerate deployment of AI with GPU chips that was also announced today.

Elastic Inference works with a range of 1 to 32 teraflops of data.

Inferentia detects when a major framework is being used with an EC2 instance, and then looks at which parts of the neural network would benefit most from acceleration; it then moves those portions to Elastic Inference to improve efficiency.

collect
0
Michael Wadsworth 2016-09-13
img

Now that Nvidia has addressed the consumer market with its latest graphics cards based on the Pascal architecture, the next solutions in the company s Pascal rollout addresses the deep neural network market to accelerate machine learning.

These solutions arrive in the form of Nvidia s new Tesla P4 and Tesla P40 accelerator cards to speed up the inferencing production workloads carried out by services that use artificial intelligence.

There are essentially two types of accelerator cards for deep neural networks: training and inference.

Inference, however, is the process of providing an input to the deep neural network and having it extract data based on that input.

According to Nvidia, the new Tesla P4 and Tesla P40 accelerator cards are designed for inferencing and include specialized inference instructions based on 8-bit operations, making them 45 times faster in response time than an Intel Xeon E5-2690v4 processor.

They also provide a 4x improvement over the company s previous generation of Maxwell Tesla cards, the M40 and M4.

collect
0
Belinda Miller 2019-04-10
img

Mobile chip-maker Qualcomm reckons all the stuff it has learned about processing AI in smartphones will come in handy in datacentres too.

The Qualcomm Cloud AI 100 Accelerator is a special chip designed to process artificial intelligence in the cloud.

Specifically Qualcomm seems to think it has an advantage when it comes to ‘AI inference’ processing – i.e.

using algorithms that have been trained with loads of data.

This stands to reason as it has its chips in millions of smart devices, all of which will have been asked to do some inference processing of their own from time to time.

“Today, Qualcomm Snapdragon mobile platforms bring leading AI acceleration to over a billion client devices,” said Qualcomm Product Management SVP Keith Kressin.

collect
0
stat Analytica 2021-08-14

We'll talk about statistical inference in this section.

Statistics is one of the fields of mathematics that deals with data collection, analysis, interpretation, and visualization of numerical data.

We will go over the definition of inference, kinds of inference, solutions, and examples of inferential statistics in depth in this post.What exactly do you mean when you say statistical inference?Statistical inference is a strategy for analyzing data and drawing inferences from it in order to account for random variations.

The basic goal of statistical inference is to forecast the sample's uncertainty or variance from sample to sample.

This provides a selection of real-value options for the specified population samples.

To give correct findings that are needed to evaluate the results of research activity, a thorough study of the data is required.

James Woodson 2021-03-19
img
A Xiaomi survey asks questions about every aspect of modern smartphones - but there's no mention of telephoto cameras.
David Clary 2019-06-24
img

A major consortium of AI community stakeholders today introduced MLPerf Inference v0.5, the group’s first suite for measurement of AI system power efficiency and performance.

Inference benchmarks are essential to understanding just how much time and power is required to deploy a neural network for common tasks like computer vision that predicts the contents of an image.

The suite consists of 5 benchmarks that include English-German machine translations with the WMT English-German data set, 2 object detection benchmarks with the COCO data set, and 2 image classification benchmarks with the ImageNet data set.

Submissions will be reviewed in September and MLPerf will share performance results in October, an organization spokesperson told VentureBeat in an email.

The inference standards were decided upon over the course of the past 11 months by partner organizations such as Arm, Facebook, Google, General Motors, Nvidia, and Toronto University, MLPerf said in a statement shared with VentureBeat.

MLPerf Inference Working Group cochair David Kanter told VentureBeat in a phone interview that benchmarks are important to using inference systems to definitively decide which solutions are worth the investment.

Joseph Cormier 2017-06-22
img

What are the chances that he has the disease?

We don't actually need to know the size of the population but it makes it easy to show you some numbers, so we'll assume a total population of two thousand; so we have 1,000 infected and 1,000 healthy.

We can summarise the figures in this table:

Let's consider simply those who, like Bob, tested positive.

So, on this showing, it looks as if the intuitive answer is correct.

Below, are those results – summarised:

Peter Williams 2019-10-15
img

Causal inference is a new language within machine learning used to help teams better understand causes and impacts so they can make better decisions.

Causal inference is only now starting to move outside the world of academics and research scientists — becoming a more relevant asset for businesses.

We’re exploring how various elements of machine learning can help break through confusing, or even conflicting, observational data and give insights that drive businesses forward.

In my experience, I see two key scenarios where companies can make use of causal inference — the planning side and the impact assessment side.

Most companies have high-level objectives like growing the user base, reducing customer churn, or increasing conversions.

It helps you zero in on the most important areas so you focus your efforts in the right place.

Joshua Herbert 2019-11-07
img

Machine-learning expert David Kanter, along with scientists and engineers from organizations such as Google, Intel, and Microsoft, aims to answer that question with MLPerf, a machine-learning benchmark suite.

Measuring the speed of machine-learning platforms is a problem that becomes more complex the longer you examine it, since both problem sets and architectures vary widely across the field of machine learning—and in addition to performance, the inference side of MLPerf must also measure accuracy.

As an example, Google trained Gmail's SmartReply feature on 238,000,000 sample emails, and Google Translate trained on trillions of samples.

If you're heavier on old-school computer science than you are on machine learning, this can be thought of as similar to the relationship between building a b-tree or other efficient index out of unstructured data and then finding the results you want from the completed index.

Performance certainly still matters when running inference workloads, but the metrics—and the architecture—are different.

The same neural network might be trained on massive supercomputers while performing inference later on budget smartphones.

John Ruybal 2018-09-19
img

(Reuters) — Alibaba will set up a dedicated chip subsidiary and aims to launch its first self-developed AI inference chip in the second half of 2019 that could be used for autonomous driving, smart cities and logistics.

The Chinese firm said at an event in Hangzhou on Wednesday that the new subsidiary would make customised AI chips and embedded processors to support the firm’s push into fast-growing cloud and internet of things (IoT) businesses.

Alibaba’s aggressive drive to develop its own semiconductors comes as China’s government looks to raise the quality of home-made chips to help propel high-tech domestic industries from cutting-edge transport to AI healthcare systems.

In April, Alibaba bought a Chinese microchip maker Hangzhou C-SKY Microsystems to help bolster its cloud-based “internet of things” (IoT) business.

Jack Ma, Alibaba co-founder and chairman, said then that China needed to control its “core technology” like chips to avoid over-reliance on U.S. imports, something which has been put in the spotlight by whipsawing trade tensions.

Michael Wadsworth 2016-09-13
img

Now that Nvidia has addressed the consumer market with its latest graphics cards based on the Pascal architecture, the next solutions in the company s Pascal rollout addresses the deep neural network market to accelerate machine learning.

These solutions arrive in the form of Nvidia s new Tesla P4 and Tesla P40 accelerator cards to speed up the inferencing production workloads carried out by services that use artificial intelligence.

There are essentially two types of accelerator cards for deep neural networks: training and inference.

Inference, however, is the process of providing an input to the deep neural network and having it extract data based on that input.

According to Nvidia, the new Tesla P4 and Tesla P40 accelerator cards are designed for inferencing and include specialized inference instructions based on 8-bit operations, making them 45 times faster in response time than an Intel Xeon E5-2690v4 processor.

They also provide a 4x improvement over the company s previous generation of Maxwell Tesla cards, the M40 and M4.

Kay Pry 2018-11-28

Amazon Web Services today announced Amazon Elastic Inference, a new service that lets customers attach GPU-powered inference acceleration to any Amazon EC2 instance and reduces deep learning costs by up to 75 percent.

“What we see typically is that the average utilization of these P3 instances GPUs are about 10 to 30 percent, which is pretty wasteful with elastic inference.

You don’t have to waste all that costs and all that GPU,” AWS chief executive Andy Jassy said on stage at the AWS re:Invent conference earlier today.

“[Amazon Elastic Inference] is a pretty significant game changer in being able to run inference much more cost-effectively.”

Amazon Elastic Inference will also be available for Amazon SageMaker notebook instances and endpoints, “bringing acceleration to built-in algorithms and to deep learning environments” the company wrote in a blog post.

It will support machine learning frameworks TensorFlow, Apache MXNet and ONNX.

Michel Smith 2021-11-02
img

By explicitly describing any implicit linkages and connections between your data sources, you may construct a more complete and accurate representation of your data.

This includes the capability to represent various definitions for the same data, enabling stakeholder engagement, and conveying situational or theoretical facts.The procedure Inference Engine for Smart Data is the next level, which is referred to as Big Data.

Specifically, it suggests the targeted and quality treatment of a big volume of data gathered in your organization, by breaking it down into small batches with the goal of obtaining actual value for your business through data and applying it to a specified objective.Capture business and domain rules in the data model; the Inference Engine implements these rules intelligently during query execution.

Oftentimes, organizations already have data models stored in database schemas, data dictionaries, or Excel files.The gathering of data without filtering is one of marketers', pricing managers', and internet business strategists' major challenges.

And it is for this reason that having tools you can rely on to choose, process, and analyses data in packs becomes more critical than ever to maintain focus.The gathering of data without filtering is one of marketers', pricing managers', and internet business strategists' major challenges.

And it is for this reason that having tools you can rely on to choose, process, and analyses data in packs becomes more critical than ever to maintain focus.Only by monitoring appropriate competition will your firm be able to avoid falling for deceptive signs about direct effect strategy.

Joe Richards 2018-09-12
img

Nvidia today debuted the Tesla T4 graphics processing unit (GPU) chip to speed up inference from deep learning systems in datacenters.

The T4 GPU packed with 2,560 CUDA cores, and 320 Tensor cores with the power to process queries nearly 40 times faster than a CPU.

Inference is the process of deploying trained AI models to power the intelligence imbued in services like visual search engines, video analysis tools, or when you ask an AI assistant like Alexa or Siri a question.

As part of its push to capture the deep learning market, two years ago, Nvidia debuted its Tesla P4 chip made especially for the deployment of AI models two years ago.

The T4 is more than 5 times faster than its predecessor the P4 at speech recognition inference and nearly 3 times faster at video inference.

Analysis by Nvidia found that nearly half of all inference performed with the P4 in the span of the past two years was related to videos, followed by speech processing, search, and natural language and image processing.

Christopher Driskell 2019-11-06
img

Today marks the release of the first results from the MLPerf Inference benchmark, which audits the performance of 594 variations of machine learning acceleration across a variety of natural language and computer vision tasks.

The benchmark is intended to create an industrywide standard for judging inference performance.

Each system takes a unique approach to inference and presents a trade-off between latency, throughput, power, and model quality, according to a white paper from organizers of the benchmark.

“The final results show a four-orders-of-magnitude performance variation ranging from embedded devices and smartphones to data-center systems,” the paper reads.

The analysis of CPUs, GPUs, and TPUs in datacenters and edge devices is the product of more than 30 organizations and 200 machine learning engineers and practitioners from Alibaba, Facebook, Google, Intel, Microsoft, Qualcomm, and Samsung.

The first MLPerf inference measured the performance of machine learning deployment tech from 14 organizations, representing 44 systems in total.

Rosalie Lee 2019-11-07
img

“If all 7.7 billion people on Earth uploaded a single photo, you could classify [them all] in under 2.5 hours for less than $600”

Google and Nvidia have both declared victory for their hardware in a fresh round of “MLPerf” AI inference benchmarking tests: Google for its custom Tensor Processing Unit (TPU) silicon, and Nvidia for its Turing GPUs.

As always with the MLPerf results it’s challenging to declare an overall AI leader without comparing apples with oranges: Alibaba Cloud also performed blisteringly strongly in offline image classification.

These performance improvements are rapidly filtering down to the enterprise level: as sophisticated customer service chatbots, models to predict investment outcomes, to underpin nuclear safety, or discover new cures for disease.

New Smartphone App for AI Inference Benchmarks

The MLPerf Inference v0.5 tests contain five benchmarks that focus on three machine learning tasks: object detection, machine translation, and image classification.

Donald Broussard 2019-04-30
img

“Thrilling” to wield this kind of inference firepower

Google Cloud says it has made NVIDIA T4 GPUs cloud instances available in eight regions, improving the ability of customers to run demanding AI inference workloads.

NVIDIA T4s, first released in October last year, are a high-end, single-slot, 6.6-inch PCI Express Gen3 Deep Learning accelerator based on the TU104 NVIDIA GPU.

They ship with 16 GB GDDR6 memory and a 70W maximum power limit and are offered as a passively cooled board that requires system air flow to operate the card.

Google’s announcement is good news for NVIDIA, which had noted “dramatic” pause in hardware spending by hyperscale cloud providers in its last earnings.

Chris Kleban, Google Cloud’s GPU product manager, wrote late Tuesday: “NVIDIA’s T4 GPU… accelerates a variety of cloud workloads, including high performance computing (HPC), machine learning training and inference, data analytics, and graphics.”

Wayne Konwinski 2018-11-28
img

Amazon today announced Inferentia, a chip designed by AWS especially for the deployment of large AI models with GPUs, that’s due out next year.

Inferentia will work with major frameworks like TensorFlow and PyTorch and is compatible with EC2 instance types and Amazon’s machine learning service SageMaker.

“You’ll be able to have on each of those chips hundreds of TOPS; you can band them together to get thousands of TOPS if you want,” AWS CEO Andy Jassy said onstage today at the annual re:Invent conference.

Inferentia will also work with Elastic Inference, a way to accelerate deployment of AI with GPU chips that was also announced today.

Elastic Inference works with a range of 1 to 32 teraflops of data.

Inferentia detects when a major framework is being used with an EC2 instance, and then looks at which parts of the neural network would benefit most from acceleration; it then moves those portions to Elastic Inference to improve efficiency.

Belinda Miller 2019-04-10
img

Mobile chip-maker Qualcomm reckons all the stuff it has learned about processing AI in smartphones will come in handy in datacentres too.

The Qualcomm Cloud AI 100 Accelerator is a special chip designed to process artificial intelligence in the cloud.

Specifically Qualcomm seems to think it has an advantage when it comes to ‘AI inference’ processing – i.e.

using algorithms that have been trained with loads of data.

This stands to reason as it has its chips in millions of smart devices, all of which will have been asked to do some inference processing of their own from time to time.

“Today, Qualcomm Snapdragon mobile platforms bring leading AI acceleration to over a billion client devices,” said Qualcomm Product Management SVP Keith Kressin.