DGEMM on Integer Matrix Multiplication Unit
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Jun 2023.
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Citing papers per year: 2026: 4.
4 papers.
- 2026Double-Precision Matrix Multiplication Emulation via Ozaki-II Scheme with FP8 Quantizationcited by 3
- 2026The Integer Alibi: Localizing Cross-Kernel Divergence in INT8-Quantized LLM Inferencecited by 2
- 2026Deterministic LLM Inference Across GPU Kernels: Power-of-Two INT8 Quantization Scales and the Limits of Tolerance-Based Conformancecited by 1
- 2026MorphingNet: Symbolic Regression Where the Network Is the Formulacited by 0
- 2026MorphingNet: Symbolic Regression Where the Network Is the Formula14 Oct 2026
- 2026Deterministic LLM Inference Across GPU Kernels: Power-of-Two INT8 Quantization Scales and the Limits of Tolerance-Based Conformance25 Aug 2026
- 2026The Integer Alibi: Localizing Cross-Kernel Divergence in INT8-Quantized LLM Inference13 Aug 2026
- 2026Double-Precision Matrix Multiplication Emulation via Ozaki-II Scheme with FP8 Quantization11 Mar 2026
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- 2025The Cambrian Explosion of Mixed-Precision Matrix Multiplication for Quantized Deep Learning Inference
- 2024Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores
- 2025APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration
- 2023M4BRAM: Mixed-Precision Matrix-Matrix Multiplication in FPGA Block RAMs
- 2026W4A16 Mixed-Precision Matrix Multiplication on Decoupled Architecture: Kernel Design and Memory Bottleneck Analysis for Ascend NPUs
- 2024Universal Matrix Multiplication on Quantum Computer
- 2021A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization
- 2023DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures using Lookup Tables