Arithmetic logic unit

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Solution Overview

Problem

Current computer architectures face inefficiencies in computational throughput for real-time image processing in driver assistance systems, particularly in handling large datasets without proportionally increasing register width and memory bandwidth.

Innovation Solution

The implementation of a SIMD arithmetic logic unit (ALU) with multiple data lanes that perform pairwise computations on input data, reducing output bits through bit reduction operations, and utilizing global control for efficient routing and function selection, allowing for operations like addition, subtraction, multiplication, and relational operations without storing intermediate results.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If multiple data lanes with functional units are implemented to increase computational throughput, then processing speed improves, but device complexity increases

Engineering Contradiction:
Improvecomputational throughputVSAvoiddevice complexity
Core Design Contradiction:
ProductivityVSDevice complexity

Solution Approach 1:

The ALU is divided into multiple independent data lanes (first data lane, second data lane, etc.), each containing functional units that can operate independently. This segmentation allows parallel processing of multiple data elements simultaneously, increasing computational throughput while keeping each lane's complexity manageable

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

Each data lane contains functional units that can perform multiple operations (addition, subtraction, multiplication, division, logical operations) on different data types. The same functional units handle various computational tasks, reducing the need for dedicated hardware for each operation and thereby controlling device complexity

Inventive Principle:
Principle #6Universality (Multi-functionality)

2Productivity

If bit reduction operations are performed to reduce output bits, then data processing efficiency improves, but information loss increases

Engineering Contradiction:
Improvedata processing efficiencyVSAvoidinformation loss
Core Design Contradiction:
ProductivityVSLoss of information

Solution Approach 1:

The bit reduction operation changes the parameter of output data width from N bits to M bits (where M < N). This parameter change enables more compact data representation and faster processing, while the selective reduction maintains essential information by preserving the most significant bits that carry the most important data

Inventive Principle:
Principle #35Parameter changes

3Adaptability or versatility

If global control block is implemented to manage routing and function selection, then operational flexibility improves, but control complexity increases

Engineering Contradiction:
Improveoperational flexibilityVSAvoidcontrol complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The global control block serves multiple functions: selecting active data lanes, choosing operational modes (addition, subtraction, multiplication, etc.), and controlling data routing. This multi-functionality consolidates control logic into a single unit, improving operational flexibility while avoiding the need for separate control circuits for each function

Inventive Principle:
Principle #6Universality (Multi-functionality)

Solution Approach 2:

The global control block dynamically adjusts the configuration of data lanes and functional units based on the current computational requirements. It can enable or disable specific lanes, select different operations, and route data differently for each computational task, providing adaptability without requiring hardware reconfiguration

Inventive Principle:
Principle #15Dynamics

4Quantity of substance

If intermediate results are not stored to reduce memory bandwidth requirements, then memory usage decreases, but computational accuracy may be affected

Engineering Contradiction:
Improvememory bandwidthVSAvoidcomputational accuracy
Core Design Contradiction:
Quantity of substanceVSMeasurement precision

Solution Approach 1:

The essential information is extracted and preserved in the reduced M-bit output format, while removing redundant less significant bits. This extraction process maintains the critical computational results needed for accuracy while eliminating data that would consume memory bandwidth without contributing to the final result

Inventive Principle:
Principle #2Taking out (Extraction)

Data Source

PatentEP2728462B1Arithmetic logic unit
Publication Date: 2019.10.23 MOBILEYE VISION TECH LTD
  • EP2728462B1 patent drawingFigure 1~2
  • EP2728462B1 patent drawingFigure 3
  • EP2728462B1 patent drawingFigure 4

AI summary

An arithmetic logic unit (320) including a first routing grid (408) connected to multiple data lanes (400) to drive first data to the data lanes (400). A second routing grid (412) is connected to the data lanes (400) to drive second data to the data lanes (400). Each of the data lanes (400) include multiple, e.g. N, functional units with first inputs from the first routing grid and second inputs from the second routing grid. The functional units compute pairwise a function of the respective first data on the respective first inputs and the respective second data on the respective second inputs. Each of the data lanes include a reduction unit with inputs adapted to receive K' bits per word from the functional units. The reduction unit is configured to perform a reduction operation configured to output an output result having a reduced number J' bits per word, wherein J' is less than N multiplied by K'.