Sparse Weight Matrix Processing for Reconfigurable Processors

The weight matrix is segmented into multiple blocks, and each block is processed independently. This segmentation allows the system to identify and utilize dense substructures within the sparse matrix, enabling better hardware resource utilization while maintaining storage efficiency. The block-wise processing structure facilitates parallel computation and optimizes the use of computing resources.

The patent transforms the weight matrix from a fully dense format to a sparse format by changing the storage parameters. It introduces a block sparse structure that changes how data is organized and accessed, allowing the system to reduce storage overhead while creating patterns that can be efficiently processed by hardware, thus resolving the contradiction between storage reduction and resource utilization.

The patent applies different processing strategies to different blocks of the weight matrix. By identifying blocks with higher density and processing them differently from sparser blocks, the method preserves important computational information locally while maintaining overall sparsity. This local quality approach ensures that algorithm accuracy is maintained in critical regions while still achieving storage reduction globally.

The patent performs preliminary analysis of the weight matrix to identify dense substructures and patterns before the main computation. This preliminary action involves examining the sparsity pattern and organizing blocks in a way that preserves computational accuracy. By preparing the data structure in advance with block-wise organization, the method ensures that subsequent computations maintain high accuracy while benefiting from reduced storage requirements.

By segmenting the weight matrix into blocks, the patent enables more efficient data access patterns. The block structure allows the system to load and process data in optimized chunks, reducing the total bandwidth required for data transmission while maintaining computing efficiency. The segmented approach facilitates better cache utilization and reduces memory access overhead.

The patent introduces a dynamic block-wise processing approach where the computation adapts to the actual data distribution. By dynamically selecting which blocks to process and how to process them based on their density and importance, the system optimizes the trade-off between bandwidth usage and computing efficiency. This dynamic approach ensures that critical computations receive adequate bandwidth while less important operations utilize reduced bandwidth.