International business machines corporation (20240202275). ASSIGNING DNN WEIGHTS TO A 3D CROSSBAR ARRAY simplified abstract
Contents
- 1 ASSIGNING DNN WEIGHTS TO A 3D CROSSBAR ARRAY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ASSIGNING DNN WEIGHTS TO A 3D CROSSBAR ARRAY - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Deep Neural Network Weight Matrix Allocation Strategies
- 1.13 Original Abstract Submitted
ASSIGNING DNN WEIGHTS TO A 3D CROSSBAR ARRAY
Organization Name
international business machines corporation
Inventor(s)
Geoffrey Burr of Cupertino CA (US)
HsinYu Tsai of Cupertino CA (US)
Irem Boybat Kara of Adliswil (CH)
Martin Michael Frank of Dobbs Ferry NY (US)
ASSIGNING DNN WEIGHTS TO A 3D CROSSBAR ARRAY - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240202275 titled 'ASSIGNING DNN WEIGHTS TO A 3D CROSSBAR ARRAY
Simplified Explanation
The patent application describes a system, method, and computer program product for efficiently assigning deep neural network weight matrices to a compute-in-memory accelerator system, specifically focusing on allocation strategies for assigning weight-layers to two-dimensional tiers of three-dimensional crossbar array tiles.
- Efficient allocation strategies optimize throughput and minimize latency and contention in a CIM accelerator system.
- Strategies include assigning weight matrices to tiers and tiles of a 3D crossbar array to maximize throughput and minimize completion latency for finite-batch-size workflows.
- Strategies also aim to minimize dead-time latency before the next batch member can be input in infinite-batch-size or continuous workflow scenarios.
Key Features and Innovation
- Efficient allocation strategies for assigning DNN weight-layers to tiers and tiles of a CIM accelerator system.
- Optimization of throughput, latency, and contention to maximize accelerator performance.
- Focus on minimizing dead-time latency in continuous workflow scenarios.
Potential Applications
The technology can be applied in various fields such as artificial intelligence, machine learning, data processing, and high-performance computing.
Problems Solved
- Minimization of contention, latency, and dead-time in a CIM accelerator system.
- Maximization of accelerator throughput for improved performance.
Benefits
- Enhanced efficiency in assigning DNN weight matrices to a CIM accelerator system.
- Improved performance and reduced latency in processing workflows.
Commercial Applications
Commercial applications of this technology could include AI accelerators, data centers, cloud computing services, and high-performance computing systems.
Prior Art
Readers can explore prior research on deep neural network weight matrix allocation strategies in CIM accelerator systems to understand the evolution of this technology.
Frequently Updated Research
Stay updated on the latest advancements in deep neural network weight matrix allocation strategies for CIM accelerator systems to ensure optimal performance and efficiency.
Questions about Deep Neural Network Weight Matrix Allocation Strategies
1. What are the key benefits of efficiently assigning DNN weight matrices to a CIM accelerator system? 2. How does the technology address the challenges of contention, latency, and dead-time in accelerator systems?
Original Abstract Submitted
a system, method and computer program product for assigning deep neural network (dnn) weight matrices to a compute-in-memory (cim) accelerator system, and particularly, efficient allocation strategies for assigning dnn model weight-layers to two-dimensional (2d) tiers of three-dimensional (3d) crossbar array tiles. such efficient allocation strategies for assigning dnn model weight-layers to tiers and tiles of a cim accelerator are optimized to minimize contention, latency and dead-time, and to maximize accelerator throughput. in one scenario, efficient allocation strategies include assigning dnn weight matrices to the 2d tiers of a 3d crossbar array tile to maximize throughput and minimize completion latency for a finite-batch-size example of an incoming workflow. in a further scenario, efficient allocation strategies assign dnn weight matrices to the 2d tiers of a 3d crossbar array tile to minimize dead-time-latency-before-next-batch-member-can-be-input in an infinite-batch-size or a continuous workflow scenario.
