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Reading list

What is Deep Learning? DL vs ML vs AI Why Deep Learning is So Popular?
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Gradient Descent Algorithm Variants of Gradient Descent Algorithm
Introduction to Loss Function Binary and Categorical Cross Entropy
Why Do We Need Activation Functions? Linear Activation Function Sigmoid and Tanh Function Softmax How to Select Right Activation Function?
Perceptron Weights in Perceptron Introduction to Artificial Neural Network
Understanding Forward Propagation Mathematically Understand Backward Propagation Mathematically Implementing Neural Networks in Python
Problems with Gradient Descent Gradient Descent with Momentum Adagrad and Adadelta
Introduction to Learning Rate Schedulers
Overview of Deep Learning Frameworks Implementing Neural Networks using Keras Functional API in Keras Implementing Neural Networks using Keras Hyperparameter Tuning of MLP in Keras
Understanding Early stopping Understanding Dropout Vanishing and Exploding Gradients Weights Initialization Techniques Implementing Weight Initializing Techniques Batch Normalization Image Augmentation Techniques Image Generator and Fit Generator Model Checkpointing Implementing Model Checkpointing Dealing with Class Imbalance Ensemble Deep Learning
Introduction to GPU and TPU TensorRT
Introduction to Unsupervised Learning How to Solve Unsupervised Learning Problems? Introduction to Autoencoders Implementing Autoencoders
A Beginners Guide to Codeless Deep Learning
TensorFlow Serving Build Deep Learning Models for Android
Introduction to PyTorch and Tensors Mathematical and Matrix Operations in PyTorch Important PyTorch Modules Implement CNN in PyTorch Transfer Learning in PyTorch Working with Text Data in PyTorch Building a RNN model in PyTorch Autoencoders in PyTorch

Google Unveils Custom Arm-based CPUs for Cloud Computing

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 11 Apr, 2024
2 min read

Google has unveiled its latest innovation, Google Axion Processors, at the Cloud Next 2024 event. These custom Arm-based CPUs are set to redefine cloud computing, offering unparalleled performance and energy efficiency. With Axion, Google aims to address the evolving demands of modern computing, from information retrieval to AI. Meanwhile, it also emphasizes sustainability and affordability. Let’s delve deeper into this promising development and its implications for the industry.

Also Read: Google Cloud Next 2024: Latest Innovations Unveiled

Axion Processors: Google's Arm-based CPUs for Cloud Computing

Evolution of Custom Silicon

Google’s journey in custom silicon began in 2015 with the introduction of Tensor Processing Units (TPUs). Since then, it has continuously innovated, releasing five generations of TPUs, a Video Coding Unit (VCU), and SoC designs for mobile devices. Now, with Axion Processors, Google reinforces its commitment to custom computing, leveraging Arm’s highest-performing CPU cores for enhanced performance and efficiency.

Also Read: Meta Unveils Its Best AI Chip Yet

Unveiling Google Axion Processors

Axion processors represent a significant leap in performance for general-purpose workloads. This includes web and app servers, microservices, databases, media processing, and even AI tasks. Powered by Arm Neoverse™ V2 CPU and supported by Titanium microcontrollers, Axion offers optimized performance and offloads platform operations. These ensure superior efficiency and scalability for customer workloads.

Implications for Cloud Computing

The introduction of Axion processors marks Google’s strategic move to compete with rivals like Amazon and Microsoft in the cloud infrastructure market. By offering industry-leading performance and energy efficiency, Google aims to attract customers seeking to maximize performance, reduce costs, and meet sustainability goals. Axion’s compatibility with existing Arm workloads further simplifies adoption, ensuring seamless integration with Google Cloud services.

Also Read: Alibaba Cloud Revolutionizes Generative AI with Serverless Solutions

Google Axion Processors: Revolutionizing Cloud Computing with Custom Arm-based CPUs

Customer and Industry Responses

Leading organizations across various sectors, including Broadcom, CrowdStrike, Datadog, Elastic, and Snap, have expressed enthusiasm for Axion processors. They anticipate significant performance gains and cost savings, reinforcing Google’s position as a key player in cloud infrastructure. Moreover, industry analysts recognize Axion as a pivotal development, enhancing Google’s competitiveness and reshaping the cloud computing landscape.

Our Say

Axion Processors is Google’s latest development showcased at the Cloud Next 2024 event. These custom Arm-based CPUs represent a paradigm shift in cloud computing, combining cutting-edge technology with a commitment to sustainability and affordability. With Axion, Google reaffirms its position as a leader in driving technological advancements that empower businesses and propel innovation forward. As Google continues to innovate and collaborate with industry partners, the future of cloud computing looks brighter than ever.

Follow us on Google News to stay updated with the latest innovations in the world of AI, Data Science, & GenAI.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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