Dynamic UMA-NUMA Memory Architecture for Workload-Adaptive Access
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Solution Overview
Problem
Traditional SoC designs face challenges in balancing memory access latency, power consumption, and memory management complexity due to the fixed choice between UMA and NUMA architectures, which are suboptimal for varying workloads.
Innovation Solution
A dynamically configurable UMA/NUMA memory architecture that adjusts the ratio of UMA and NUMA regions based on workload changes, using a configurable boundary point to optimize performance and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If UMA architecture is used, then memory management complexity is reduced, but memory access latency increases and power consumption increases
Solution Approach 1:
The memory system is segmented into multiple memory regions, each associated with different processing elements. This segmentation allows NUMA architecture to provide lower access latency for local memory regions while UMA manages global memory regions, resolving the contradiction between complexity and latency.
Solution Approach 2:
Different memory regions are assigned different access characteristics - local memory regions provide fast access for nearby processing elements while global regions provide uniform access. This local quality differentiation allows the system to optimize for both low latency and manageable complexity simultaneously.
2Loss of time
If NUMA architecture is used, then memory access latency is reduced, but memory management complexity increases
Solution Approach 1:
The memory system is segmented into multiple memory regions, each associated with different processing elements. This segmentation allows NUMA architecture to provide lower access latency for local memory regions while UMA manages global memory regions, resolving the contradiction between complexity and latency.
3Device complexity
If UMA architecture is used, then memory management complexity is reduced, but power consumption increases
Solution Approach 1:
Different memory regions are assigned different access characteristics - local memory regions provide fast access for nearby processing elements while global regions provide uniform access. This local quality differentiation allows the system to optimize for both low latency and manageable complexity simultaneously.
4Device complexity
If fixed memory architecture is used, then design simplicity is maintained, but adaptability to varying workloads deteriorates
Solution Approach 1:
The memory architecture transitions from a fixed design to a dynamic one where the configurable boundary point can be adjusted based on workload characteristics. This allows the system to adapt between UMA and NUMA configurations, maintaining design simplicity while achieving workload adaptability.
Solution Approach 2:
The boundary point between UMA and NUMA regions is made configurable, allowing the system to change memory access parameters dynamically. This parameter change capability enables the system to adapt to different workload requirements without changing the fundamental hardware architecture.
Data Source
AI summary
The description relates to dynamic memory management. One example includes an assembly that entails processing elements and memory. A dynamic UMA/NUMA configuration module is configured to facilitate managing a first region of the memory based upon a Uniform Memory Access (UMA) architecture and a second region of the memory based upon a Non-Uniform Memory Access (NUMA) architecture. The dynamic UMA/NUMA configuration module is configured to dynamically adjust ratios of the memory in the first region and the second region based upon workload changes on the processing elements.