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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

Microsoft & AMD Set To Challenge NVIDIA in AI Chip Manufacturing

Yana Khare
Yana Khare Last Updated : 10 May, 2023
3 min read
NVIDIA Corp. has joined with Microsoft Corp. and Advanced Micro Devices Inc. (AMD) to develop a rival to its graphics processing units (GPUs) | AI-capable processor

The market leader in AI-capable processors, NVIDIA Corp., has joined with Microsoft Corp. and Advanced Micro Devices Inc. (AMD) to develop a rival to its graphics processing units (GPUs). The action is part of a more significant initiative to increase AI processing capacity. It is crucial, given the popularity of chatbots and other services like ChatGPT.

Also Read: Revolutionizing Computer Graphics: NVIDIA to Unveil 20 AI Research Papers at SIGGRAPH 2023

Partnership with AMD

According to sources, Microsoft supports AMD’s efforts by offering technical resources and collaborating with the chipmaker on a proprietary Microsoft processor for AI workloads. The company is said to have invested roughly $2 billion in its chip efforts, and several hundred workers are working on the Athena project.

Also Read: Microsoft Building AI Chip’ Athena’ to Boost ChatGPT and AI Tech

Microsoft’s Deepening Involvement in the Chip Industry

Microsoft's Deepening Involvement in the Chip Industry

Microsoft’s growing participation in the semiconductor sector reflects its role as a leading provider of cloud computing services and a pioneer in using AI. It aims to integrate AI elements across its whole product range and has invested $10 billion into OpenAI, which makes ChatGPT.

Impact on Microsoft’s Programs

Microsoft’s partnership with OpenAI necessitates more processing capacity than the corporation anticipated when it placed chip orders and built up data centers. Businesses interested in integrating OpenAI’s ChatGPT service into their goods or internal programs have expressed interest.

At the same time, Microsoft has unveiled a chat-based Bing and new AI-enhanced Office features. NVIDIA’s expensive and potent processors are necessary for all those AI applications operated in Microsoft’s Azure cloud.

Also Read: Microsoft Hits Another Blow to Google With Massive Update to Bing Chat

Challenges in Developing an Alternative to NVIDIA’s Lineup

Creating a selection that competes with NVIDIA’s won’t be easy. Chips, a programming language, networking devices, and servers are just a few of the hardware and software components NVIDIA delivers in a coordinated package that enables users to enhance their capabilities quickly. This is one of the factors contributing to NVIDIA’s rise to prominence in the AI chip industry.

Competition from Other Cloud Rivals

But Microsoft isn’t the only company attempting to create its AI-capable processors. Amazon, a competitor in the cloud space, bought Annapurna Labs in 2016 and has since made two separate AI processors. Google, a subsidiary of Alphabet Inc., also has its training chip.

Also Read: Google VS Microsoft: The Battle of AI Innovation

Microsoft’s Continued Collaboration with NVIDIA

Microsoft wants to continue collaborating closely with NVIDIA, whose processors power AI systems throughout training and execution. Additionally, it’s looking for methods to obtain more NVIDIA chips, highlighting the critical scarcity that Microsoft and others are experiencing.

Our Say

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Microsoft’s partnership with AMD to provide a graphics processing unit (GPU) alternative to NVIDIA’s GPUs demonstrates its dedication to boosting AI processing capacity. With the popularity of ChatGPT and other chatbots, demand for these highly sought-after components has skyrocketed. Thus,  necessitating the urgent necessity for NVIDIA’s competitors. Although creating an internal AI-capable processor will be difficult, Microsoft is pursuing this objective with cloud rivals Amazon and Google. The action highlights its growing participation in the semiconductor sector as it works to acquire more of the parts needed to power its cloud computing services and artificial intelligence (AI) initiatives.
 Also Read: How Did A Taiwanese Company Become The Backbone Of Modern AI?
AIAI chipsAI-capable processorsAMDchatbotsChatGPTCloudMicrosoftNVIDIA
Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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