Heterogeneous Multi-Processing
GPTKB entity
Statements (53)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:microprocessor
|
| gptkbp:bfsLayer |
7
|
| gptkbp:bfsParent |
gptkb:Exynos_5_Dual
gptkb:Exynos_5260 gptkb:Exynos_5420 gptkb:Samsung_Exynos_5430 gptkb:Exynos_5410 gptkb:Exynos_5_Octa |
| gptkbp:benefits |
Machine learning
Scientific simulations Graphics rendering |
| gptkbp:challenges |
Resource management
Programming complexity Data transfer overhead |
| gptkbp:developed_by |
Various semiconductor companies
|
| gptkbp:enables |
Parallel processing
|
| gptkbp:enhances |
Latency
Throughput User experience |
| gptkbp:facilitates |
Task scheduling
|
| gptkbp:game_components |
gptkb:CEO
|
| https://www.w3.org/2000/01/rdf-schema#label |
Heterogeneous Multi-Processing
|
| gptkbp:improves |
Energy efficiency
|
| gptkbp:is_adopted_by |
gptkb:musician
gptkb:robot Augmented reality Automotive applications |
| gptkbp:is_evaluated_by |
Performance metrics
Benchmark tests |
| gptkbp:is_explored_in |
gptkb:Workshops
Conferences Research papers |
| gptkbp:is_implemented_in |
System-on-chip (So C) designs
|
| gptkbp:is_influenced_by |
Trends in AI
Trends in Io T Trends in cloud computing |
| gptkbp:is_related_to |
gptkb:software
gptkb:CUDA |
| gptkbp:is_supported_by |
gptkb:software
Operating systems Development tools Simulation environments |
| gptkbp:is_used_in |
Mobile devices
High-performance computing Embedded systems |
| gptkbp:key |
Scalability
Performance optimization Resource utilization Future computing paradigms |
| gptkbp:receives_funding_from |
gptkb:API
DSP |
| gptkbp:requires |
Software optimization
|
| gptkbp:supports |
Diverse workloads
|