Data Truncation with Multi-Precision Retrieval for Memory Bottlenecks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern computing workloads, particularly those involving large data processing like machine learning and AI applications, face memory retrieval bottlenecks due to the speed at which data is retrieved from memory, which can decrease overall computational throughput.
Innovation Solution
Implementing a system that truncates data into multiple precision levels by splitting it into portions, allowing retrieval at different levels of precision based on computational needs, with the option to add adjustment values for accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is retrieved from memory at high speed, then computational throughput is improved, but memory retrieval bottleneck decreases overall computational throughput
Solution Approach 1:
The patent segments data into multiple precision levels (e.g., full precision and truncated precision versions). This allows the system to retrieve only the necessary precision level from memory, reducing the volume of data transferred and alleviating the memory retrieval bottleneck, thereby improving computational throughput without sacrificing necessary accuracy.
Solution Approach 2:
The patent changes the precision parameter of data stored in memory by maintaining multiple versions of the same data at different precision levels. This enables dynamic selection of data precision based on computational requirements, allowing faster retrieval for less critical computations while preserving full precision when needed, thus resolving the throughput-speed contradiction.
2Measurement precision
If data is stored at full precision, then accuracy is maintained, but memory usage and retrieval time increase
Solution Approach 1:
The patent segments data storage into multiple precision levels, storing both full precision and truncated precision versions. This segmentation allows the system to retrieve data at the minimum necessary precision level, reducing retrieval time while maintaining accuracy when full precision is required. The segmented storage structure enables selective retrieval based on computational needs.
Solution Approach 2:
The patent applies partial action by retrieving only the necessary portion of data precision required for each computation. Instead of always retrieving full precision data, the system retrieves truncated precision versions when sufficient for the task, reducing retrieval time. Full precision data is retrieved only when absolutely necessary, optimizing the balance between accuracy and speed.
3Speed
If data is truncated to reduce precision, then retrieval speed increases, but data fidelity decreases
Solution Approach 1:
The patent implements dynamic precision selection where the system can adaptively choose between truncated and full precision data based on the specific computational task requirements. This dynamic approach allows retrieval speed to be optimized for tasks that don't require full precision, while automatically switching to full precision when data fidelity is critical, thus resolving the speed-fidelity contradiction.
Solution Approach 2:
The patent changes the precision parameter of stored data by maintaining multiple versions at different precision levels. This parameter variation enables the system to select the appropriate precision level for each retrieval operation, achieving faster retrieval speeds when lower precision suffices while preserving the option to retrieve high-fidelity data when needed.
4Measurement precision
If full precision data is always retrieved, then accuracy is maintained, but power consumption increases
Solution Approach 1:
The patent applies partial action by retrieving only the necessary precision level required for each computational task. Instead of always retrieving full precision data, the system retrieves truncated precision versions when sufficient, reducing the amount of data transferred and processed, thereby lowering power consumption while maintaining adequate accuracy for the task at hand.
Solution Approach 2:
The patent changes the precision parameter of retrieved data based on task requirements. By dynamically adjusting the precision level from full to truncated, the system reduces the energy required for data transfer and processing while maintaining sufficient accuracy, thus resolving the contradiction between accuracy and power consumption.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems and methods for data truncation are disclosed. A computing system may a storage medium (104, 106) and a processor (102). The processor (102) may be configured to identify first data of a first data structure; identify a first portion (304a) of the first data and a second portion (304b) of the first data; store the first portion (304a) of the first data as a second data structure in the storage medium (104, 106), and the second portion (304b) of the first data as a third data structure in the storage medium (104, 106); identify a request for the first data; based on the request, retrieve from the storage medium (104, 106) the second data structure including the first portion (304a) of the first data; identify a value associated with the second portion (304b); generate second data based on the first portion (304a) and the value; and perform a task based on the second data.