Deep learning hardware achieves high throughput and low power consumption by reducing computing precision and specializing in matrix multiplication.
For machine learning inference, fixed-point value computation is commonplace, where the input and output values and the model parameters are quantized.
Thus, many processors are now equipped with fast integer matrix multiplication units (IMMU) .
It is of significant interest to find a way to harness these IMMUs to improve the performance of HPC applications while maintaining accuracy.
We focus on the Ozaki scheme, which computes a high-precision matrix multiplication by using lower-precision computing units, and show the advantages and disadvantages of using IMMU.
The experiment using integer Tensor Cores shows that we can compute double-precision matrix multiplication faster than cuBLAS and an existing Ozaki scheme implementation on FP16 Tensor Cores on NVIDIA consumer GPUs.
Furthermore, we demonstrate accelerating a quantum circuit simulation by up to 4.33 while maintaining the FP64 accuracy.