Memory Filter Component for Reducing Data Throughput Bottlenecks
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Solution Overview
Problem
The dominant performance bottleneck in computing systems is the data transfer throughput from memory, exacerbated by conventional von Neumann architectures and existing solutions that require software awareness of data locality and mapping.
Innovation Solution
Implementing a memory system with a filter component that receives responses to memory operation messages, identifies filterable conditions, and filters responses to reduce data throughput, without requiring software awareness of data locality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is transferred from memory to processing units using conventional von Neumann architecture, then processing can be performed on the data, but data transfer throughput becomes the dominant performance bottleneck
Solution Approach 1:
The patent extracts only the necessary data from memory responses by implementing a filter component that selectively passes through only those data elements that satisfy specified filter conditions. This prevents unnecessary data from being transferred across the communication path, directly addressing the throughput bottleneck caused by transferring all memory data regardless of need.
Solution Approach 2:
The filter component performs filtering actions on memory responses before the data is transferred to processing units. By evaluating filter conditions and condensing data in advance within the memory system, the patent reduces the volume of data that needs to be transferred, thereby improving overall processing throughput without being constrained by data transfer speed limitations.
2Loss of energy
If existing solutions for reducing data transfer are implemented, then data transfer overhead is reduced, but software awareness of data locality and mapping is required
Solution Approach 1:
The memory system performs self-service by automatically filtering and condensing data within the memory subsystem itself, without requiring external software intervention for data locality management. The filter component autonomously evaluates conditions and selects relevant data, eliminating the need for software to be aware of data locality and mapping while still reducing data transfer overhead.
Solution Approach 2:
The filter component acts as an intermediary between the memory array and the processing units, automatically managing data selection and condensation. This intermediary handles the complexity of data locality and mapping internally, allowing software to remain simple while achieving reduced data transfer overhead through automatic filtering of memory responses.
3Reliability
If all memory responses are transferred to processing units, then complete data availability is ensured, but data traffic in the communication path increases
Solution Approach 1:
The filter component applies local quality filtering by evaluating specific conditions for each data element in the memory response and selectively passing through only those elements that meet the criteria. This ensures that the processing units receive precisely the data they need for their operations, maintaining data availability for required operations while minimizing unnecessary data traffic in the communication path.
Data Source
AI summary
In accordance with described techniques for filtered responses to memory operation messages, a computing system or computing device includes a memory system that receives messages. A filter component in the memory system receives the responses to the memory operation messages, and filters one or more of the responses based on a filterable condition. A tracking logic component tracks the one or more responses as filtered responses for communication completion.