Microsoft technology licensing, llc (20240126611). Workload-Aware Hardware Architecture Recommendations simplified abstract
Contents
- 1 Workload-Aware Hardware Architecture Recommendations
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Workload-Aware Hardware Architecture Recommendations - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
Workload-Aware Hardware Architecture Recommendations
Organization Name
microsoft technology licensing, llc
Inventor(s)
Amar Phanishayee of Seattle WA (US)
Divya Mahajan of Seattle WA (US)
Janardhan Kulkarni of Redmond WA (US)
Miguel Castro of Cambridge (GB)
Muhammad Adnan of Vancouver (CA)
Workload-Aware Hardware Architecture Recommendations - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240126611 titled 'Workload-Aware Hardware Architecture Recommendations
Simplified Explanation
The patent application describes a method for optimizing accelerator architectures for deep learning models by splitting the operator graph into portions and tuning accelerator cores for each portion.
- The method involves obtaining a deep learning training script and extracting an operator graph from it.
- The operator graph is split into first and second portions of a heterogeneous pipeline.
- A first accelerator core is tuned for the first portion and a second accelerator core for the second portion.
- A hardware architecture is generated with the tuned accelerator cores to collectively accomplish the deep learning model.
Potential Applications
This technology could be applied in various industries such as healthcare for medical image analysis, autonomous vehicles for object recognition, and finance for fraud detection.
Problems Solved
This technology addresses the challenge of optimizing accelerator architectures for deep learning models to improve performance and efficiency.
Benefits
The benefits of this technology include faster training times, improved accuracy of deep learning models, and reduced energy consumption.
Potential Commercial Applications
"Optimizing Accelerator Architectures for Deep Learning Models: Applications and Benefits"
Possible Prior Art
Prior art in this field may include research papers or patents related to optimizing hardware architectures for deep learning models.
Unanswered Questions
How does this method compare to existing techniques for optimizing accelerator architectures for deep learning models?
The article does not provide a direct comparison with existing techniques in the field.
What are the specific performance improvements achieved by tuning the accelerator cores for different portions of the operator graph?
The article does not detail the specific performance improvements achieved through this tuning process.
Original Abstract Submitted
the description relates to accelerator architectures for deep learning models. one example can obtain a deep learning training script associated with a deep learning model and extract an operator graph from the training script. the example can split the operator graph into first and second portions of a heterogeneous pipeline and tune a first accelerator core for the first portion of the heterogeneous pipeline and a second accelerator core for the second portion of the heterogeneous pipeline. the example can also generate a hardware architecture that includes the first accelerator core and the second accelerator core arranged to collectively accomplish the deep learning model.
