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1. Nowadays, deep neural networks achieve outstanding performance on applications such as image classification and object detection. To handle huge amount of calculation efficiently, we should choose suitable architecture for performing these deep learning (DL) applications. (a) What is the bottleneck of traditional instruction sets and corresponding general-purpose processors (e.g., single-core processors) for executing the calculation of DL applications? (b) Some x86 architectures which support instruction set extensions for multimedia, e.g., AVX, are used to accelerate these DL applications. What are the advantage(s) and limitation(s) of the instruction set extensions? (c) Graphics processing units (GPUS) are usually the first choice for DL computation. What type(s) of parallelism does a GPU intend to exploit or take advantage of? How does a GPU exploit the type(s) of parallelism?

1. Nowadays, deep neural networks achieve outstanding performance on applications such as image classification and object detection. To handle huge amount of calculation efficiently, we should choose suitable architecture for performing these deep learning (DL) applications. (a) What is the bottleneck of traditional instruction sets and corresponding general-purpose processors (e.g., single-core processors) for executing the calculation of DL applications? (b) Some x86 architectures which support instruction set extensions for multimedia, e.g., AVX, are used to accelerate these DL applications. What are the advantage(s) and limitation(s) of the instruction set extensions? (c) Graphics processing units (GPUS) are usually the first choice for DL computation. What type(s) of parallelism does a GPU intend to exploit or take advantage of? How does a GPU exploit the type(s) of parallelism?

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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1. Nowadays, deep neural networks achieve outstanding performance on applications such as image classification and object detection. To handle huge amount of calculation efficiently, we should choose suitable architecture for performing these deep learning (DL) applications. (a) What is the bottleneck of traditional instruction sets and corresponding general-purpose processors (e.g., single-core processors) for executing the calculation of DL applications? (b) Some x86 architectures which support instruction set extensions for multimedia, e.g., AVX, are used to accelerate these DL applications. What are the advantage(s) and limitation(s) of the instruction set extensions? (c) Graphics processing units (GPUS) are usually the first choice for DL computation. What type(s) of parallelism does a GPU intend to exploit or take advantage of? How does a GPU exploit the type(s) of parallelism?
Transcribed Image Text:1. Nowadays, deep neural networks achieve outstanding performance on applications such as image classification and object detection. To handle huge amount of calculation efficiently, we should choose suitable architecture for performing these deep learning (DL) applications. (a) What is the bottleneck of traditional instruction sets and corresponding general-purpose processors (e.g., single-core processors) for executing the calculation of DL applications? (b) Some x86 architectures which support instruction set extensions for multimedia, e.g., AVX, are used to accelerate these DL applications. What are the advantage(s) and limitation(s) of the instruction set extensions? (c) Graphics processing units (GPUS) are usually the first choice for DL computation. What type(s) of parallelism does a GPU intend to exploit or take advantage of? How does a GPU exploit the type(s) of parallelism?
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