In this project, we tackled the inefficiencies of matrix multiplication on CPUs due to their general-purpose architectures and limited internal registers. Our objective was to design, verify, and synthesize a hardware accelerator that minimizes data transfers and leverages parallel processing for efficient matrix multiplication. This accelerator is particularly suited for applications in machine learning, where large-scale linear algebra operations are common.
Our design aims to address the core challenge of matrix multiplication - the movement of data between main memory and the computing units. We focused on:
- Implementing a hardware accelerator for matrix multiplication (A × B + C).
- Minimizing data transfers by ensuring each data element is moved only once and is used in all relevant computations.
- Achieving significant parallelism by utilizing systolic arrays for the processing elements (PEs).
- Hardware Design: The accelerator is designed as an array of multiply or multiply-accumulate processing elements arranged in a systolic array architecture for minimal data transfers.
- Software Control: A software layer controls the hardware accelerator, handling data movement from memory to the design using AHB to APB transactions.
- Verification and Synthesis: The design was verified and synthesized using HDL-Designer and QuestaSim, ensuring compatibility and performance.
- Reconfigurable Dimensions: Supports reconfigurable nk and m dimensions, with constraints to ensure efficient processing.
- Intermediate Result Handling: Capable of holding and using intermediate results as needed.
- Error Reporting: Includes mechanisms for detecting overflows and reporting errors through a specific output port.
The project submission included:
- A Verilog-2005 top-level module named
matmul. - A comprehensive design document detailing the design's block diagram, functional description, flow-chart/state-machine diagram, and any deviations from standard design-checker rules.
The evaluation focused on RTL code quality, comprehensive documentation, and the design's ability to compile with various parameter values.
This project represents a significant step towards optimizing matrix multiplication processes by reducing the reliance on CPU architecture limitations. By designing a specialized hardware accelerator, we demonstrated an innovative approach to achieving faster and more efficient matrix calculations, crucial for the computational demands of modern machine learning applications.