Kernel Mapping to Nodes in Reconfigurable Compute Fabric
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer architectures face limitations in performance and efficiency due to the time and energy required for data movement between processors and memory, constraining compute systems beyond the capabilities of transistor scaling.
Innovation Solution
The implementation of a memory-compute system with a hybrid threading processor and fabric that enables compute-near-memory architecture, allowing processors to be integrated with or near memory components, and using a reconfigurable compute fabric to optimize resource usage and reduce latency through cost-function-based resource mapping and asynchronous data flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is moved between processors and memory using conventional bus architecture, then system compatibility and ease of manufacture are maintained, but performance and energy efficiency deteriorate due to significant time and energy requirements
Solution Approach 1:
The system is divided into multiple processing nodes, each with its own local memory, forming a distributed memory-compute system. This segmentation eliminates the single shared memory bottleneck by allowing parallel data processing across multiple independent nodes, thereby reducing data movement time and improving compute performance.
Solution Approach 2:
The patent transitions from a centralized memory architecture to a distributed three-dimensional stacking architecture. Memory devices are stacked vertically above processing nodes using 3D integration techniques, creating a new spatial dimension for data access that dramatically reduces data movement distance and time while maintaining system compatibility.
2Productivity
If data is moved between processors and memory using conventional bus architecture, then system simplicity is maintained, but energy efficiency deteriorates due to significant energy consumption
Solution Approach 1:
By segmenting the system into distributed memory-compute nodes, each node processes data locally without requiring energy-intensive data transfers across the bus. This segmentation reduces energy consumption by eliminating redundant data movement while improving compute efficiency through parallel processing.
Solution Approach 2:
The patent introduces an intermediary mapping layer that optimizes data flow between memory and compute resources. This intermediary intelligence routes data through the most energy-efficient paths in the 3D stacked architecture, reducing overall energy consumption while maintaining high compute efficiency.
3Speed
If processors are integrated near memory components in a memory-compute system, then data access speed is improved, but device complexity increases due to reconfigurable compute fabric and node integration
Solution Approach 1:
The processing nodes are designed with universal, reconfigurable compute fabrics that can be dynamically configured for different compute tasks. This multi-functionality allows the same hardware structure to handle various workloads efficiently, reducing the need for specialized complex architectures while maintaining high data access speeds.
Solution Approach 2:
The system uses software-defined configuration to change operational parameters of the compute fabric dynamically. By adjusting configuration parameters rather than physical architecture, the system achieves high data access speed with manageable complexity through software-controlled adaptability.
4Adaptability or versatility
If reconfigurable compute fabric is used to optimize resource usage, then resource utilization is improved, but mapping complexity increases due to kernel mapping requirements
Solution Approach 1:
The system implements self-service automated mapping mechanisms that dynamically allocate compute resources based on workload characteristics. The mapping complexity is handled automatically by the system's resource management intelligence, allowing high resource optimization without requiring manual mapping complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor resource utilization and dynamically adjust kernel mappings. This feedback-driven adaptation optimizes resource usage while the automated feedback loop manages mapping complexity, eliminating the need for complex manual mapping configurations.
Data Source
AI summary
A reconfigurable compute fabric can include multiple nodes, and each node can include multiple tiles with respective processing and storage elements. Compute kernels can be parsed into directed graphs and mapped to particular node or tile resources for execution. In an example, a branch-and-bound search algorithm can be used to perform the mapping. The algorithm can use a cost function to evaluate the resources based on capability, occupancy, or power consumption of the various node or tile resources.