PF-GEMV: Utilization maximizing architecture in fast matrix-vector multiplication for GPT-2 inference

Abstract

Owing to the widespread advancement of transformer-based artificial neural networks, artificial intelligence (AI) processors are now required to perform matrix-vector multiplication in addition to the conventional matrix-matrix multiplication. However, current AI processor architectures are optimized for general matrix-matrix multiplications (GEMMs), which causes significant throughput degradation when processing general matrix-vector multiplications (GEMVs). In this study, we proposed a port-folding GEMV (PF-GEMV) scheme employing multiformat and low-precision techniques while reusing an outer product-based processor optimized for conventional GEMM operations. This approach achieves 93.7% utilization in GEMV operations with an 8-bit format on an 8 X 8 processor, thus resulting in a 7.5 X increase in throughput compared with that of the original scheme. Furthermore, when applied to the matrix operation of the GPT-2 large model, an increase in speed by 7 X is achieved in single-batch inferences.

Keywords

• low-precision
• matrix-vector multiplication
• multiformat
• utilization