Semiconductor Device With Shared Registers For Unified Processing
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Solution Overview
Problem
Existing semiconductor systems face challenges in implementing a single processor capable of integrated image processing, vision processing, and neural network processing due to differences in data processing rate, memory bandwidth, and synchronization requirements among these applications, making it difficult to provide a unified processing environment.
Innovation Solution
A semiconductor device with a first processor for region of interest (ROI) calculations and a second processor for arithmetic calculations, both sharing registers and instruction set architecture, along with a load store unit, data arrange layer, and multiple arithmetic logic units to efficiently process image data in a unified manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple isolated processors are used for image processing, vision processing and neural network processing, then each application can have dedicated processing capabilities, but the system complexity increases and data utilization efficiency decreases
Solution Approach 1:
The patent combines multiple isolated processors into a single integrated processor that can handle image processing, vision processing, and neural network processing. This is achieved by implementing a unified architecture with shared functional units, registers, and memory interfaces, allowing different processing tasks to be performed by the same hardware resources rather than requiring separate dedicated processors for each application.
Solution Approach 2:
The patent creates a universal processor architecture where the same processing units can be dynamically allocated to different processing tasks. The processor includes configurable functional units that can adapt their operation mode based on the required application, enabling a single processor to replace multiple specialized processors while maintaining the necessary processing capabilities for image, vision, and neural network applications.
2Adaptability or versatility
If multiple isolated processors are used for image processing, vision processing and neural network processing, then each application can have dedicated processing capabilities, but data utilization efficiency decreases
Solution Approach 1:
The patent merges multiple processors into one integrated unit with shared data paths, memory interfaces, and functional units. This consolidation allows data to be processed multiple times by different functional units without requiring repeated memory accesses, significantly improving data utilization efficiency. The shared architecture enables intermediate results to be reused across different processing stages and applications.
Solution Approach 2:
The integrated processor architecture enables continuous processing by allowing data to flow through multiple functional units in sequence without interruption. The shared memory and data paths ensure that data remains available for subsequent processing operations, eliminating the idle time and repeated data loading that would occur in a multi-processor isolated architecture, thus maintaining continuous useful action across different processing tasks.
3Device complexity
If a single integrated processor is used for image processing, vision processing and neural network processing, then system complexity is reduced, but it becomes difficult to satisfy the different requirements of each application
Solution Approach 1:
The patent implements a dynamic processor architecture where functional units can be dynamically configured and allocated based on the specific requirements of each application. The processor includes configurable processing elements that can adjust their operation mode, precision, and resource allocation in real-time to match the needs of image processing, vision processing, or neural network processing tasks, allowing a single integrated processor to satisfy different application-specific requirements.
Solution Approach 2:
The integrated processor incorporates specialized functional units with different characteristics optimized for specific processing tasks. For example, certain ALU groups are configured with properties suitable for image processing while others are optimized for neural network operations. This local differentiation within the unified architecture allows each application to utilize the most appropriate processing resources while maintaining overall system integration and reduced complexity.
4Adaptability or versatility
If separate processors are used for different processing applications, then processing requirements can be met, but memory access and power consumption increase
Solution Approach 1:
The patent merges multiple processors into a single integrated unit with shared memory interfaces and data paths. This consolidation eliminates redundant memory accesses that would occur in a multi-processor system, as data can be loaded once and then processed by multiple functional units within the same processor. The shared architecture also allows for power management optimizations where inactive functional units can be powered down, reducing overall power consumption while maintaining the necessary processing capabilities.
Data Source
AI summary
A semiconductor device including a first processor having a first register, the first processor configured to perform region of interest (ROI) calculations using the first register; and a second processor having a second register, the second processor configured to perform arithmetic calculations using the second register. The first register is shared with the second processor, and the second register is shared with the first processor.