DMA Data Reduction Core for CPU Offload and Throughput
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Solution Overview
Problem
In systems with limited resources, such as user terminals or servers, the increasing data throughput requirements for big data and AI applications lead to resource constraints, necessitating efficient data processing solutions to accelerate data handling.
Innovation Solution
A data reduction device and method that includes a control module, DMA engine, and data reduction core to perform hash and compression operations on request data, with a traffic scheduler and completion controller to optimize operations, allowing for offloading of CPU-intensive tasks and improving data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data processing is performed using traditional CPU-based methods, then processing capability is maintained, but power consumption increases and processing speed decreases under resource constraints
Solution Approach 1:
The data processing function is segmented into two distinct processing units: a CPU for control and coordination tasks, and a dedicated data reduction core for intensive data processing operations. This segmentation allows each unit to specialize in its optimal function, with the data reduction core handling power-intensive operations while the CPU manages overall system control, thereby improving processing speed while managing power consumption efficiently.
Solution Approach 2:
A data reduction core is introduced as an intermediary processing unit between the CPU and storage/memory systems. This intermediary handles the computationally intensive data reduction operations, freeing the CPU from these tasks and enabling it to focus on higher-level control functions. The data reduction core acts as a mediator that bridges the gap between raw data and processed information, improving overall system productivity.
2Productivity
If data throughput is increased to meet big data and AI requirements, then processing capability improves, but resource constraints are exacerbated
Solution Approach 1:
The system architecture is segmented into specialized processing units, with the data reduction core dedicated to handling high-volume data throughput operations. This segmentation enables the system to process large amounts of data without overloading the CPU, as the data reduction core independently manages the intensive processing tasks, thereby maintaining high throughput while preserving CPU resources for other functions.
Solution Approach 2:
The data reduction core operates autonomously to handle data processing tasks, performing self-service by independently executing data reduction operations without requiring constant CPU intervention. This self-service capability allows the system to maintain high data throughput while minimizing the resource burden on the CPU, as the data reduction core manages its own operational workflow and resource allocation.
3Ease of operation
If CPU-intensive operations are performed directly by the CPU, then processing control is simplified, but processing speed and efficiency decrease
Solution Approach 1:
The data reduction core serves as an intermediary that handles CPU-intensive operations, allowing the CPU to maintain simplified control logic while delegating heavy processing tasks. The CPU issues high-level commands to the data reduction core, which then autonomously executes the intensive operations, thereby maintaining ease of operation at the CPU level while significantly improving processing efficiency through specialized hardware acceleration.
Solution Approach 2:
CPU-intensive operations are extracted from the main CPU processing pipeline and offloaded to a dedicated data reduction core. This extraction removes the computational burden from the CPU, allowing it to focus on control and coordination tasks with simplified logic, while the extracted intensive operations are handled by the specialized data reduction core, thereby improving overall processing efficiency without complicating CPU control.
Data Source
AI summary
A data reduction device, a data reduction method, and a system including the data reduction device are provided. The data reduction device includes a control module configured to generate a control signal in response to a first kernel request received from an external entity, a direct memory access (DMA) engine configured to read request data corresponding to the first kernel request from an external entity in response to the control signal, and a data reduction core configured in such a manner that the request data is reduced through a first operation to be generated first reduction data or the first reduction data is reduced through a second operation to be generated as second reduction data.