Sparse Tensor Decoding with Sparsity Bitmaps for AI Accelerators
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Solution Overview
Problem
Existing neural network accelerators face inefficiencies due to the large memory and energy overhead required for storing and processing zero-value-compressed tensor data, particularly in mobile devices, which are constrained by size, temperature, and power limitations.
Innovation Solution
An in-line sparsity-aware tensor data distribution system that decodes zero-value-compression data vectors using flexible scheduling and on-the-fly sparsity bitmap reconstruction, reducing data movement and energy consumption by storing only non-zero elements in local registers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If zero-value-compression is applied to store only non-zero elements, then memory storage efficiency is improved, but decoding complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing sparsity bitmaps alongside the compressed tensor data. These bitmaps are generated in advance to mark the positions of non-zero elements, enabling the decoding process to directly access and retrieve only the necessary data elements without complex computation, thus resolving the contradiction between compression efficiency and decoding complexity
Solution Approach 2:
The patent introduces sparsity bitmaps as an intermediary data structure that mediates between the compressed tensor storage and the decoding process. These bitmaps serve as a lookup mechanism that simplifies the decoding operation by providing direct position information for non-zero elements, eliminating the need for complex search algorithms and reducing processing time
2Speed
If traditional memory storage is used for tensor data, then processing speed is maintained, but energy consumption and memory overhead increase
Solution Approach 1:
The patent extracts and stores only the non-zero elements of tensor data in a compressed format, removing redundant zero-value storage. By combining this extraction with efficient sparsity-aware retrieval mechanisms, the system reduces memory bandwidth requirements and energy consumption while maintaining processing speed for the actual computational elements
Solution Approach 2:
The patent changes the storage parameter from dense format to sparse compressed format, and changes the retrieval parameter from sequential access to sparsity-guided direct access. These parameter changes enable the system to process only relevant data elements, reducing energy consumption proportionally to the sparsity ratio while maintaining processing speed through optimized access patterns
3Productivity
If sparsity bitmaps are pre-computed and stored, then decoding efficiency is improved, but memory overhead increases
Solution Approach 1:
The patent applies partial action by computing and storing sparsity bitmaps only for the portions of tensor data that will be accessed, rather than pre-computing for entire datasets. This selective approach provides sufficient decoding efficiency for practical workloads while minimizing the memory overhead associated with storing comprehensive sparsity information
Data Source
AI summary
Methods, systems, articles of manufacture, and apparatus are disclosed to decode zero-value-compression data vectors. An example apparatus includes: a buffer monitor to monitor a buffer for a header including a value indicative of compressed data; a data controller to, when the buffer includes compressed data, determine a first value of a sparse select signal based on (1) a select signal and (2) a first position in a sparsity bitmap, the first value of the sparse select signal corresponding to a processing element that is to process a portion of the compressed data; and a write controller to, when the buffer includes compressed data, determine a second value of a write enable signal based on (1) the select signal and (2) a second position in the sparsity bitmap, the second value of the write enable signal corresponding to the processing element that is to process the portion of the compressed data.