Most Resource Efficient Matrix Vector Multiplication on FPGAs - Lehnert, Alexander; Holzinger, Philipp; Pfenning, Simon; Müller, Ralf; Reichenbach, Marc (IEEE Access, 2023)
Fast and resource-efficient inference in artificial neural networks (ANNs) is of utmost importance and drives many new developments in the area of new hardware architectures, e.g., by means of systolic arrays or algorithmic optimization such as pruning. In this paper, we present a novel method for lowering the computation effort for ANN inference utilizing ideas from information theory.Weight matrices are sliced into submatrices of logarithmic aspect ratios. These slices are then factorized. This reduces the number of required computations without compromising on fully parallel processing. We create a new hardware architecture for this dedicated purpose. We also provide a tool to map these sliced and factorized matrices efficiently to reconfigurable hardware. By comparing to the state of the art FPGA implementations, we can prove our claim by lowering hardware resources measured in look-up-tables (LUTs) by a factor of three to six. Our method does not rely on any particular property of the weight matrices of the ANN. It works for the general task of multiplying an input vector with a constant matrix and is also suitable for digital signal processing beyond ANNs.
CRIS-Team Scopus Importer:2023-02-10
Keywords: Computational Efficiency; Computer Architecture; Computer architecture; Constant Matrix Multiplication; Encoding; Field programmable gate arrays; Hardware; Matrix decomposition; Neural Networks; Reconfigurable Architectures; Signal processing algorithms; Sparse matrices