Which of the following can improve the computational efficiency of the neural network model? (Multjple choice)

Question

Options

• ATPU
• BGPU
• CFPGA
• DLarge-scale distributed cluster

• A
  100% (44)

Explanation

All four options are correct because each represents a hardware or infrastructure approach that accelerates neural network computation through different means. GPUs (B) use massively parallel architectures originally designed for graphics, making them highly efficient at the matrix multiplications central to neural networks. TPUs (A) are custom ASICs designed specifically by Google for tensor operations, offering even greater efficiency than GPUs for certain deep learning workloads. FPGAs (C) are reprogrammable chips that can be tailored to a specific model's architecture, reducing latency and power consumption for inference tasks. Large-scale distributed clusters (D) improve efficiency by parallelizing training across many machines simultaneously, enabling faster processing of large datasets and models.

Since all choices are correct, there are no distractors in this question - every listed option is a legitimate and widely used technique.

Memory tip: Think of the acronym "TGF-D" - TPU, GPU, FPGA, Distributed. Each solves the same bottleneck (slow computation) at a different level: custom chip, general parallel chip, reconfigurable chip, and networked machines.

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