Vector Processor SoC for Infrared Camera Electronics
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Solution Overview
Problem
Conventional infrared camera electronics architectures are costly, inefficient, and unable to provide the necessary processing speed and flexibility for modern infrared imaging systems, leading to increased power consumption, size, and complexity due to the combination of general-purpose processors, programmable logic devices, and hardwired electronics.
Innovation Solution
A multi-vector processor architecture with a system-on-a-chip (SoC) implementation that includes vector processors for parallel processing of pixel data, local memories for efficient data access, and a general-purpose processor for system management, enabling scalable and flexible image processing while reducing the number of discrete components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional general-purpose processors are used for pixel processing, then programming flexibility is improved, but processing speed deteriorates
Solution Approach 1:
The system divides processing tasks into two segments: a general-purpose processor handles high-level functions and algorithm configuration, while dedicated vector processing units handle pixel-level parallel processing. This segmentation allows each component to excel at its specific function, resolving the contradiction between flexibility and speed.
Solution Approach 2:
A programmable logic device acts as an intermediary between the general-purpose processor and the vector processing units. It translates high-level algorithms from the general-purpose processor into optimized hardware configurations for the vector units, enabling both flexibility and high-speed processing.
2Adaptability or versatility
If conventional programmable logic devices are used for pixel processing, then programming flexibility is improved, but logic density deteriorates
Solution Approach 1:
The system segments the processing architecture so that the programmable logic device is responsible only for algorithm configuration and control logic, while complex pixel processing is offloaded to dedicated vector processing units. This reduces the logic density requirement for the programmable device while maintaining flexibility.
Solution Approach 2:
Complex pixel processing operations are replaced by dedicated hardware vector processing units that execute instructions in parallel. This substitutes the mechanical reconfiguration of programmable logic with optimized hardware execution, improving logic density while preserving programmability through the control device.
3Productivity
If hardwired electronics are used for pixel processing, then processing speed is improved, but cost and configurability deteriorate
Solution Approach 1:
The system combines static vector processing units (for speed) with a dynamically reconfigurable programmable logic device (for adaptability). The programmable device can load different algorithms and configurations at runtime, allowing the system to adapt to different processing requirements while maintaining high-speed execution through the vector units.
Solution Approach 2:
The vector processing units are designed as universal parallel processors that can execute multiple types of pixel processing algorithms through programmable control. This multi-functionality allows a single hardware architecture to perform various image processing tasks at high speed without requiring custom hardwired circuits for each algorithm.
4Productivity
If multiple discrete components are combined to meet processing demands, then processing capability is improved, but cost, size, and power requirements worsen
Solution Approach 1:
The system merges the general-purpose processor, programmable logic device, vector processing units, and memory into a single integrated system-on-a-chip architecture. This consolidation reduces the number of discrete components, simplifies interconnections, and improves power efficiency while maintaining the processing capabilities provided by each individual component.
Solution Approach 2:
The integrated architecture uses universal vector processing units that can handle multiple pixel processing algorithms, reducing the need for separate dedicated hardware blocks for each function. This multi-functionality decreases overall system complexity while maintaining high processing capability across different image processing tasks.
Data Source
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AI summary
Systems and methods are disclosed herein to provide infrared imaging systems with improved electronics architectures. In one embodiment, an infrared imaging system is provided that includes an infrared imaging sensor for capturing infrared image data and a main electronics block for efficiently processing the captured infrared image data. The main electronics block may include a plurality of vector processors each configured to operate on multiple pixels of the infrared image data in parallel to efficiently exploit pixel-level parallelism. Each vector processor may be communicatively coupled to a local memory that provides high bandwidth, low latency access to a portion of the infrared image data for the vector processor to operate on. The main electronics block may also include a general-purpose processor configured to manage data flow to/from the local memories and other system functionalities. The main electronics block may be implemented as a system-on-a-chip.