Hardware Accelerators for ML Inference

Learn more about different types of hardware accelerators that can be used to speed up ML inference.

Hardware Accelerators for ML Inference

Machine learning (ML) inference refers to the process of using trained ML models to make predictions or decisions based on new data. ML inference can be computationally intensive, particularly for large models and datasets, and so hardware acceleration can be used to speed up the process.

Hardware Accelerators

There are several different kinds of hardware that can be used to accelerate ML inference, including:

  1. Central processing units (CPUs): CPUs are the "brain" of a computer and are responsible for executing instructions and performing tasks. While CPUs are not as specialized as other hardware accelerators, they are widely available and can be used to perform ML inference.
  2. Graphics processing units (GPUs): GPUs are specialized hardware designed for efficient processing of graphics and parallel computations. They are commonly used to accelerate ML inference because they can perform many calculations simultaneously, making them well-suited for tasks such as matrix operations that are common in ML.
  3. Tensor processing units (TPUs): TPUs are Google's proprietary hardware accelerators designed specifically for ML. They are optimized for the matrix computations required by deep learning algorithms, and can provide significant performance improvements over CPUs and GPUs.
  4. Field-programmable gate arrays (FPGAs): FPGAs are reconfigurable chips that can be programmed to perform specific tasks, such as ML inference. They offer low latency and high energy efficiency, and can be customized to specific ML models and workloads.
  5. Application-specific integrated circuits (ASICs): ASICs are chips that are specifically designed and built for a specific application, such as ML inference. They can offer very high performance, but are typically more expensive and inflexible than other hardware options.

Overall, the choice of hardware for ML inference depends on factors such as the type of model, the workload, and the cost and performance trade-offs. By choosing the right hardware accelerator, it is possible to significantly speed up ML inference and improve the performance of ML systems.

Video Overview

Listen to this tech talk for an overview of the many interesting types of hardware used in training and running ML models. Learn when to use each of them and why.