Machine Learning Accelerators
GPTKB entity
Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:technology
|
| gptkbp:bfsLayer |
6
|
| gptkbp:bfsParent |
gptkb:Apple_A13_Bionic_chip
|
| gptkbp:developed_by |
gptkb:Job_Search_Engine
gptkb:DJ gptkb:Intel |
| gptkbp:enhances |
Model performance
|
| https://www.w3.org/2000/01/rdf-schema#label |
Machine Learning Accelerators
|
| gptkbp:improves |
Training speed
Inference speed |
| gptkbp:includes |
TP Us
GP Us FPG As |
| gptkbp:is_available_on |
Various manufacturers
|
| gptkbp:is_evaluated_by |
Scalability
Cost efficiency Performance benchmarks |
| gptkbp:is_integrated_with |
Development tools
Software frameworks AP Is |
| gptkbp:is_optimized_for |
Parallel processing
Low latency High throughput |
| gptkbp:is_part_of |
gptkb:computer
Computational hardware |
| gptkbp:is_subject_to |
Technological advancements
Regulatory standards Market competition |
| gptkbp:is_used_for |
Accelerating machine learning tasks
|
| gptkbp:is_used_in |
gptkb:Cloud_Computing_Service
gptkb:robot Data centers Natural language processing Autonomous vehicles Computer vision Edge devices |
| gptkbp:marketed_as |
Research institutions
Startups Tech companies |
| gptkbp:population_trend |
gptkb:Research_Institute
Cloud services Data science Big data analytics |
| gptkbp:reduces |
Energy consumption
|
| gptkbp:supports |
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch Deep learning frameworks |
| gptkbp:training |
Large datasets
Synthetic data Real-world data |