Nearest Neighbor Unit for Parallel Distance Calculation
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Solution Overview
Problem
Modern processors face challenges in executing complex instructions efficiently, particularly those requiring more resources like floating-point operations and data moves, which can slow down overall processing throughput.
Innovation Solution
The implementation of a processor architecture that includes a nearest neighbor unit (NNLU) and specific instruction handling for nearest neighbor calculations, utilizing a reduced search space table and duplicate removal circuit to accelerate computations, allowing for parallel processing and efficient execution of distance measurements between input data and dictionary atoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general-purpose processors execute complex instructions like floating-point operations and data moves, then computational versatility is maintained, but processing throughput decreases
Solution Approach 1:
The processor is divided into multiple specialized execution units: a nearest-neighbor execution unit for distance calculations and dictionary operations, a separate general-purpose execution unit for other computational tasks, and a conversion unit for data type transformations. This segmentation allows each unit to be optimized for its specific function, enabling parallel execution and maintaining high throughput while preserving computational versatility.
Solution Approach 2:
A conversion unit acts as an intermediary between the nearest-neighbor execution unit and the general-purpose execution unit. This intermediary handles data type conversions and result transformations, allowing the specialized unit to operate independently without blocking the general-purpose unit, thus maintaining processing throughput while achieving versatile computation.
2Device complexity
If traditional processors perform nearest neighbor calculations using standard instruction sequences, then hardware complexity is minimized, but computational speed decreases
Solution Approach 1:
The nearest-neighbor execution unit pre-calculates distance metrics and identifies potential nearest neighbors before the general-purpose unit performs final selection and processing. This preliminary action accelerates the overall computation by preparing data in advance, achieving high computational speed without requiring excessive hardware complexity.
Solution Approach 2:
The execution units utilize register files and buffer memories to create temporary copies of data during processing. This allows parallel operations on multiple data elements simultaneously without repeatedly accessing main memory, significantly improving computational speed while keeping hardware complexity manageable through efficient use of on-chip storage.
3Adaptability or versatility
If processors handle large datasets for machine learning and compressive sensing applications, then application capability is enhanced, but power consumption increases
Solution Approach 1:
By segmenting the processing workload between a specialized nearest-neighbor unit and a general-purpose unit, each unit processes only the operations it is optimized for. This reduces unnecessary computational overhead and power consumption while maintaining the ability to handle large datasets for machine learning and compressive sensing applications.
Solution Approach 2:
The patent replaces traditional sequential mechanical processing with parallel computational operations in the nearest-neighbor execution unit. This substitution enables simultaneous distance calculations for multiple data points, reducing the total processing time and average power consumption while enhancing application capability for large-scale data processing.
Data Source
AI summary
A processor includes a front end to decode an instruction, an allocator to pass the instruction to a nearest neighbor logic unit (NNLU) to execute the instruction, and a retirement unit to retire the instruction. The NNLU includes logic to determine input of the instruction for which nearest neighbors will be calculated, transform the input, retrieve candidate atoms for which the nearest neighbors will be calculated, compute distance between the candidate atoms and the input, and determine the nearest neighbors for the input based upon the computed distance.