Tensor Access Hybrid Tiled Im2col Quantization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current techniques for accessing large multi-dimensional tensors in deep learning applications face inefficiencies due to tile quantization in tiled techniques and high memory bandwidth requirements in im2col methods, leading to performance degradation and suboptimal resource utilization.
Innovation Solution
A hybrid approach combining tiled and im2col techniques to access tensors, where tensors are split into tiles and im2col portions, allowing for efficient loading and processing that minimizes tile quantization and memory bandwidth issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If tiled technique is used to access tensors, then memory bandwidth requirements are reduced, but tile quantization occurs leading to performance degradation
Solution Approach 1:
The patent segments the tensor access process into two distinct parts: tiled access for the majority of tensor elements and im2col access for the remaining elements that do not fit into complete tiles. This segmentation allows each technique to be applied where it is most effective, avoiding the limitations of using either technique alone.
Solution Approach 2:
The patent dynamically changes the access technique parameter based on the tensor dimensions and tile size. When tensor dimensions are not evenly divisible by tile size, the system transitions from pure tiled access to a hybrid approach incorporating im2col access, thereby adapting to varying parameter conditions to optimize performance.
2Productivity
If im2col method is used to access tensors, then tile quantization is avoided, but memory bandwidth requirements increase
Solution Approach 1:
The patent applies im2col access partially, only for the portion of tensor elements that cannot be evenly divided into tiles. The majority of elements are accessed using the more memory-efficient tiled technique, while im2col is used as a supplementary method for the remaining elements, thus avoiding excessive memory bandwidth consumption.
Solution Approach 2:
The patent segments the tensor elements into two groups: those that fit into complete tiles and those that do not. This segmentation enables selective application of access techniques, using tiled access for memory efficiency and im2col access only where necessary to avoid quantization issues.
3Use of energy by moving object
If pure tiled access is used, then memory bandwidth is optimized, but resource utilization becomes suboptimal due to quantization
Solution Approach 1:
The patent changes the access technique parameter from fixed tiled access to a dynamic hybrid approach based on tensor dimension parameters. When tensor dimensions are not evenly divisible by tile size, the system adapts by incorporating im2col access, thereby improving resource utilization while maintaining memory bandwidth optimization.
Solution Approach 2:
The patent introduces dynamics into the tensor access process by allowing the access technique to change based on runtime conditions such as tensor dimensions and tile size. This dynamic adaptation enables optimal resource utilization across varying workloads while maintaining memory efficiency.
Data Source
AI summary
Apparatuses, systems, and techniques to access a multidimensional tensor from memory while minimizing tile quantization is disclosed. In at least one embodiment, a processor includes one or more circuits to cause a first one or more portions of at least one tensor to be accessed from a memory using a first technique and a second one or more portions of the at least one tensor to be accessed from the memory using a second technique based, at least in part, on an input to combine the first and second techniques.