SIMD DMA Transfer Mapping for Low-Latency Parallel Processing
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Solution Overview
Problem
Conventional processing accelerators, such as vector processing units, face inefficiencies due to system latencies in memory access and inefficient configuration techniques, particularly in SIMD operations, which hinder the performance of parallel processing algorithms in robotic systems like automated vehicles.
Innovation Solution
A multi-dimensional SIMD processor is configured to receive and load data from memory and execute SIMD instructions, reducing complexity and improving computational efficiency by mapping higher-level instructions to operations performed by the processor, thereby enhancing interoperability with higher-order systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional memory access techniques are used in SIMD operations, then system latency is reduced, but computational efficiency deteriorates
Solution Approach 1:
The patent segments the data transfer process by implementing separate load and store units that operate independently from the SIMD processing elements. This segmentation allows data to be loaded into and stored from vector registers without blocking the computational pipeline, thereby reducing system latency while maintaining high computational efficiency through concurrent operations.
Solution Approach 2:
The patent implements preliminary action by pre-loading data into vector registers before SIMD operations are executed. The load unit fetches data in advance and stores it in dedicated vector registers, so that when computational operations are initiated, the data is already available in the processing elements, eliminating wait time and improving computational throughput.
2Device complexity
If conventional configuration techniques are used for accelerators, then device complexity is reduced, but interoperability with higher-order systems deteriorates
Solution Approach 1:
The patent introduces an intermediary component that acts as a bridge between higher-order systems and the SIMD accelerator. This intermediary handles instruction mapping and data format conversion, allowing conventional accelerators to interface with modern programming models without increasing the accelerator's internal complexity, thereby improving interoperability while maintaining simplicity.
Solution Approach 2:
The patent implements universality by designing the accelerator configuration system to handle multiple data types and instruction formats through a unified interface. The load and store units are designed to accommodate various data formats and addressing modes, enabling the same hardware structure to serve multiple functions and improve compatibility with diverse higher-order systems without complicating the core architecture.
3Ease of operation
If data is frequently transferred between memory and processing elements, then data availability is improved, but system latency increases
Solution Approach 1:
The patent merges the load and store functions into unified units that are tightly coupled with the SIMD processing elements. By combining data movement operations with computational operations in an integrated architecture, the system maintains high data availability for processing while minimizing the time penalty through streamlined data paths and reduced overhead compared to separate memory access operations.
Data Source
AI summary
In various examples, systems and methods are disclosed that relate to programming multi-dimensional single instruction, multiple data (SIMD) processors (also referred to as an accelerator). In one example, a processor can obtain instructions to be performed by the accelerator. The processor can determine one or more operations to be performed by the accelerator based at least on the instructions and generate a set of accelerator instructions. In examples, the processor can then provide data associated with the accelerator instructions to cause the accelerator to perform at least a portion of the one or more operations.