RNN Weight Matrix Storage in FPGA Embedded Block RAM
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Recurrent neural network (RNN) models face low data sequence processing efficiency due to the small capacity of first-level caches, which require frequent copying of weight matrices, limiting their ability to process all data simultaneously.
Innovation Solution
Copying the weight matrix of an RNN model to an embedded block RAM of a field-programmable gate array (FPGA) allows sequential processing of data sequences using an activation function, with the weight matrix stored in the embedded block RAM, reducing the need for frequent copying and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the weight matrix is stored in the first level cache of CPU or GPU, then the data processing speed is fast, but the cache capacity is small and cannot store all weight matrices simultaneously
Solution Approach 1:
The patent transitions from using CPU/GPU first-level cache (traditional memory hierarchy) to using FPGA embedded block RAM (hardware-based memory), representing a dimensional change in the storage architecture. This allows the system to bypass the capacity limitations of conventional caches while maintaining fast access speeds through hardware-level memory operations in the FPGA.
2Productivity
If the weight matrix is copied to the first level cache for each data piece processing, then the processing can be performed, but the frequent copying leads to low data sequence processing efficiency
Solution Approach 1:
The patent loads the weight matrix into the FPGA embedded block RAM in advance, before the actual data sequence processing begins. This preliminary action eliminates the need for repeated copying operations during processing, as the weight matrix remains resident in the FPGA's fast memory throughout the entire data sequence processing pipeline.
Solution Approach 2:
By maintaining the weight matrix continuously loaded in the FPGA embedded block RAM, the system enables uninterrupted processing of the data sequence. The continuous availability of the weight matrix in fast hardware memory eliminates idle time between processing operations, allowing the useful action of data transformation to proceed without interruption.
Data Source
AI summary
The present application discloses a method and apparatus for processing a data sequence. A specific implementation of the method includes: receiving an inputted to-be-processed data sequence; copying a weight matrix in a recurrent neural network model to an embedded block random access memory (RAM) of a field-programmable gate array (FPGA); processing sequentially each piece of to-be-processed data in the to-be-processed data sequence by using an activation function in the recurrent neural network model and the weight matrix stored in the embedded block RAM; and outputting a processed data sequence corresponding to the to-be-processed data sequence. This implementation improves the data sequence processing efficiency of the recurrent neural network model.