Which architecture can process many input features in parallel?

Which architecture can process many input features in parallel?

• A. Convolutional Neural Networks
• B. Recurrent Neural Networks
• C. Transformers
• D. Generative Adversarial Network

Answer: (C)

Processing many input features in parallel is achieved by attention-based architectures, especially transformers. In a transformer, the self-attention mechanism computes relationships between every pair of positions in the input at once, allowing each position’s representation to be updated by considering all other positions simultaneously. This means you can compute outputs for all positions in parallel, leveraging GPU efficiency and enabling scalable handling of long sequences. Convolutional networks do parallelize calculations across the input, but they rely on fixed-size kernels that focus on local neighborhoods; capturing long-range dependencies typically requires many stacked layers. Recurrent networks, on the other hand, process data sequentially, step by step, which inherently limits parallelism. Generative Adversarial Networks aren’t built around parallel processing of a sequence’s features in the same way; they’re composed of two networks for generation and discrimination rather than a mechanism for parallel feature mixing. So, the mechanism that best supports processing many input features in parallel is the transformer’s self-attention, which computes interactions across all input positions in a single pass.

Processing many input features in parallel is achieved by attention-based architectures, especially transformers. In a transformer, the self-attention mechanism computes relationships between every pair of positions in the input at once, allowing each position’s representation to be updated by considering all other positions simultaneously. This means you can compute outputs for all positions in parallel, leveraging GPU efficiency and enabling scalable handling of long sequences.

Convolutional networks do parallelize calculations across the input, but they rely on fixed-size kernels that focus on local neighborhoods; capturing long-range dependencies typically requires many stacked layers. Recurrent networks, on the other hand, process data sequentially, step by step, which inherently limits parallelism. Generative Adversarial Networks aren’t built around parallel processing of a sequence’s features in the same way; they’re composed of two networks for generation and discrimination rather than a mechanism for parallel feature mixing.

So, the mechanism that best supports processing many input features in parallel is the transformer’s self-attention, which computes interactions across all input positions in a single pass.