Which hardware are specialized circuits explicitly designed to accelerate deep learning workloads?

Which hardware are specialized circuits explicitly designed to accelerate deep learning workloads?

• A. Graphics Processing Units (GPUs)
• B. Tensor Processing Units (TPUs)
• C. Central Processing Units (CPUs)
• D. Network Interface Cards

Answer: (B)

Focusing on specialized hardware for neural network workloads, Tensor Processing Units are built specifically to accelerate deep learning computations. They’re custom-designed to perform the dense tensor operations that dominate neural networks—like large-scale matrix multiplications and convolutions—very efficiently, with architecture and memory systems tuned for high throughput and energy efficiency. This specialization means TPUs typically deliver faster inference and training for DL models than general-purpose processors. Graphics processing units, while extremely capable and widely used for deep learning because of their parallelism, are general-purpose accelerators rather than devices built solely for DL. Central processing units are even more general-purpose, handling a wide range of tasks but not optimized for the particular workloads of neural networks. Network interface cards handle data movement over networks, not compute for DL. So the option that matches a hardware explicitly designed to accelerate deep learning workloads is Tensor Processing Units.

Focusing on specialized hardware for neural network workloads, Tensor Processing Units are built specifically to accelerate deep learning computations. They’re custom-designed to perform the dense tensor operations that dominate neural networks—like large-scale matrix multiplications and convolutions—very efficiently, with architecture and memory systems tuned for high throughput and energy efficiency. This specialization means TPUs typically deliver faster inference and training for DL models than general-purpose processors. Graphics processing units, while extremely capable and widely used for deep learning because of their parallelism, are general-purpose accelerators rather than devices built solely for DL. Central processing units are even more general-purpose, handling a wide range of tasks but not optimized for the particular workloads of neural networks. Network interface cards handle data movement over networks, not compute for DL. So the option that matches a hardware explicitly designed to accelerate deep learning workloads is Tensor Processing Units.