Artificial intelligence (AI) and its highly active branch of machine learning (ML) are being applied across every major industry, bringing automated intelligent decision-making and processing.
- This report will help enterprises of all kinds that are using or plan to use AI systems to make the right choices about AI hardware acceleration.
Features and Benefits
- Learn why training deep learning neural network systems is best performed on GPUs.
- Identify new players in the field.
Key questions answered
- How important is the availability of data processing tools and development software stack for the accelerator?
- Are there benchmarks available for assessing the different AI accelerators?
Table of contents
Recommendations for enterprises
Recommendations for vendors
There is no single best hardware AI accelerator
Data processing for ML applications
Choosing the right AI accelerator for the task
Defining Table 1 entries
Benchmarking AI hardware is essential to move the field forward
MLPerf is the first vendor and university benchmarking initiative for AI hardware