This article is compiled from natlawreview by Semiconductor Industry (ID: ICVIEWS)
Based on 5 questions, explain the role of GPU in enhancing AI and machine learning technology.
In the early 2000s, researchers realized that GPUs could provide a more efficient alternative to CPU-based computing for machine learning, since machine learning algorithms typically have the same types of computations as graphics processing algorithms. Despite CPU-based availability and cost constraints in recent years, GPU-based computing has become the de-facto standard for machine learning or neural network training. Explain the role of GPU in enhancing AI and machine learning technology based on 5 questions.
What is a GPU?
As the name suggests, specialized graphics processing units (GPUs) were originally designed decades ago to efficiently perform common operations such as image and video processing. These courses feature matrix-based mathematical calculations. People are generally more familiar with the central processing unit (CPU), which is found in laptops, mobile phones and smart devices and can perform many different types of operations.
In the early 2000s, researchers realized that GPUs could provide a more efficient alternative to CPU-based computing for machine learning, since machine learning algorithms typically have the same types of computations as graphics processing algorithms. Despite CPU-based availability and cost constraints in recent years, GPU-based computing has become the de-facto standard for machine learning or neural network training.
What are the benefits of using a GPU?
The key benefit of using a GPU is high efficiency. The computational efficiency provided by the GPU does more than simplify the analysis process, it facilitates broader model training for greater accuracy, broadens the scope of the model search process to prevent substitution specifications, and enables certain models that were previously unattainable becomes feasible and allows for additional sensitivity to alternative datasets for robustness.
How does a GPU support expert testimony?
AI-based systems replace human decision-making with data-driven decisions. This can lead to less subjectivity and errors when dealing with large amounts of complex information. We leverage artificial intelligence and machine learning to drive the automation of increasingly complex tasks and unlock new analytical methods, including the use of supervised and unsupervised learning, all powered by our in-house GPUs.
How does the data science center utilize GPUs for computing?
We use GPUs at all stages of the case lifecycle, from discovery to economic analysis, and for all types of data, from standard tabular data to text and images. Some of these applications rely on applications where GPU computing is already widely used, such as neural networks, while others rely on more custom analysis frameworks.
Here are some examples:
Matrix Operations
GPUs allow us to perform custom matrix operations very quickly. For example, in antitrust problems, we often need to calculate the distances (coordinate pairs) between all suppliers and all consumers. Migrating the computation from the CPU to the GPU enabled us to compute the distance between nearly 100 million coordinate pairs per second.
deep neural network
Much of the focus around GPU-based computing has been on neural networks. While capable of handling conventional classification and regression problems, additional task-specific neural network architectures provide a framework for specific analysis of text, images, and sound. Given the complexity of these models and the amount of data required to generate reliable results, their use is practically impossible without GPU computing resources. When training a popular multi-class image model on the GPU, we experienced a 25,000% speedup compared to running the same process on a single CPU. We exploit this efficiency in content analysis for the problem of consumer fraud, where we design text and image classifiers to characterize the target audience of problematic marketing materials.
Enhanced tree
As GPU computing becomes more commonplace, popular machine learning software packages are increasingly including GPU-based computing options in their products. We often use boosted trees in regression and classification problems. These models in turn aggregate multiple simple decision trees into a larger, more accurate learner. Compared to deep neural networks, which can have hundreds of millions of parameters, these models are smaller and thus require less data and training time to produce generalizable inferences. These advantages make them more useful than deep neural networks in many types of analysis that we often encounter. Switching to a GPU-based training process enables us to train models for these tasks nearly 100 times faster than the corresponding CPU specification.
language model
Language models, usually based on one or more deep learning techniques, can classify, parse, and generate text. We use large-scale language models to extract specific pieces of information, parse relationships between entities, identify semantic relationships, and complement traditional term-based features in text classification problems such as quantifying social media sentiment around public entities in defamation incidents .
Unsurprisingly, given all the things these models can do, utilizing the CPU to process documents through these models introduces significant delays to the analysis process. Using just a single GPU, we can split documents into independent components and fully process hundreds of sentences per second.
What developments in this field can we expect in the future?
GPUs and the software associated with them will continue to evolve. New hardware may have more cores, faster cores, and more memory to accommodate larger models and data batches. New software may make it easier to share models and data across multiple GPUs.
Other developments may involve entirely different equipment. To address some of the remaining inefficiencies in GPU computing, machine learning practitioners are increasingly turning to application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs). For example, Google's Tensor Processing Unit (TPU) is an ASIC designed specifically to perform computations for its machine learning TensorFlow software package. FPGAs offer greater flexibility and are often used to deploy machine learning models in production environments where low latency, high bandwidth, and minimal energy consumption are required.
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