Why in the news?
Meta is in advanced talks with Google to use its Tensor Processing Units for large scale AI workloads, indicating a major shift in the AI chip ecosystem. This led to a drop in Nvidia’s stock due to concerns over market share loss.
What is a TPU
• A specialized hardware chip designed to accelerate artificial intelligence and machine learning processing
• Developed by Google in 2016
• Optimized for tensor computations used in deep learning
• Widely deployed in data centers and cloud platforms
Why TPUs are Important
• Deep learning models require high-speed matrix and tensor calculations
• CPUs are optimized for general-purpose tasks
• GPUs are effective for parallel graphics and AI workloads
• TPUs surpass them in efficiency for specific deep learning operations
How TPUs Work
• Built to handle large scale tensor and matrix computations
• Use massive parallelism to execute numerous operations simultaneously
• Consume less energy while delivering high throughput
• Include specialized circuits to avoid unnecessary general-purpose processing overhead
What are GPU and TPU?GPU: general-purpose parallel compute processor (Used by Navidia) TPU: AI-specific chip optimised for deep learning tensor operations |
| With the present state of development, Artificial Intelligence can effectively do which of the following? (2020)
(1) Bring down electricity consumption in industrial units (2) Create meaningful short stories and songs (3) Disease diagnosis (4) Text-to-Speech Conversion (5) Wireless transmission of electrical energy Select the correct answer using the code given below: (a) 1, 2, 3 and 5 only (b) 1, 3 and 4 only (c) 2, 4 and 5 only (d) 1, 2, 3, 4 and 5 |
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