A Reconfigurable Computer Architecture Framework for Accelerating Parallel Matrix Computation in General-Purpose Processors
Keywords:
Reconfigurable computing, parallel matrix computation, computer architecture, general-purpose processor, coarse-grained reconfigurable architecture, CGRA, matrix multiplication, processing elementsAbstract
Matrix computation is a core and computationally demanding category of operations in today's computing landscape. Key operations such as matrix multiplication, matrix-vector multiplication, convolutional transformations, and factorizations are integral to fields like scientific computing, artificial intelligence, machine learning, computer vision, engineering simulation, graph analytics, signal processing, and high-performance computing. While modern general-purpose processors are equipped with multicore execution, SIMD/vector extensions, multilevel cache hierarchies, and multithreading capabilities, their static hardware design often fails to fully leverage the structural nuances of matrix workloads. Variations in matrix size, dimensionality, sparsity, data locality, precision needs, and parallelism patterns can result in suboptimal use of traditional execution resources. Recent advancements in coarse-grained reconfigurable architectures (CGRAs), FPGA-based accelerators, heterogeneous processors, and matrix-specific computing engines highlight that reconfigurability can effectively balance the flexibility of software with the efficiency of specialized hardware. Nonetheless, many current solutions are tailored to specific applications, necessitate distinct accelerator platforms, concentrate mainly on either dense or sparse computations, or offer limited integration with general-purpose processor execution environments. Recent reviews also pinpoint unresolved issues in reconfigurable matrix acceleration, such as memory access, workload imbalance, resource utilization, programmability, and adaptive design-space selection [1]–[4].
This study introduces a Reconfigurable Parallel Matrix Computing Framework (RPMCF), conceived as a closely integrated architectural extension to enhance matrix-oriented workloads on general-purpose processors. The framework integrates a host processor with a reconfigurable processing-element array, a configurable interconnection network, hierarchical local memory, an adaptive task scheduler, a matrix profiler, a runtime configuration manager, and a software programming interface. Rather than viewing matrix acceleration as a static fixed-function operation, the proposed framework dynamically adjusts the organization of processing elements, tiling strategy, execution mode, dataflow, memory allocation, and precision configuration based on workload characteristics. Both dense and sparse matrix workloads are analyzed prior to execution and assigned to different computational configurations, such as block-parallel, SIMD-style, pipelined reduction, and sparse-aware execution modes.
The research employs a design-and-evaluation approach that includes workload characterization, architectural modeling, RTL-based functional modeling, parallel execution simulation, benchmark testing, and comparative performance analysis. A practical prototype evaluation is conducted using representative square and rectangular dense matrices, along with structured sparse matrices, to evaluate execution time, throughput, speedup, processing-element utilization, memory efficiency, and estimated energy efficiency. The experimental findings suggest that the proposed framework enhances matrix-processing performance by dynamically aligning hardware resources with workload structure. For dense workloads, the architecture gains mainly from tiled data reuse, parallel multiply-accumulate pipelines, and minimized synchronization overhead. In the case of sparse and irregular workloads, adaptive work distribution and sparse-aware scheduling help reduce processing-element idle time.
The findings reveal that reconfigurable execution offers a viable compromise between fully general-purpose processors and highly specialized accelerators. In the tested configurations, the proposed framework achieves significant speedup over a baseline sequential processor model and noticeable improvement over a fixed parallel architecture, while retaining configurability across various matrix shapes and computational characteristics. The study provides a unified architectural model, adaptive mapping methodology, implementation workflow, and performance analysis for incorporating reconfigurable matrix acceleration into general-purpose computing systems. This framework is particularly pertinent to future heterogeneous processors where adaptable accelerators function alongside traditional CPU cores.
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© 2026
International Journal of Computer Engineering and Embedded Technologies.
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.