Matrix multiplication plays an important role in various territories. The input data density leads to low computation efficiency, and synchronous circuits fail to meet the low-power requirement of specific fields. Therefore, an asynchronous matrix multiplication accelerator is proposed, which selects the appropriate calculation method by sensing the data density. For SpGEMM, a two-way condensation technique is adopted to solve the problem of spoiling the right matrix input reuse. An “one-to-one” merge strategy to reduce the uncertainty of the merge process is further proposed. Finally, the area of the accelerator is 17.3 \(mm^{2}\) , and the power demand is only 0.00468W in the UMC 110nm process. This research evaluates the accelerator on the SuiteSparse set and random matrix set, achieving 3.4Œ and 3.1Œ speed-up and 86Œ and 304Œ energy saving over MKL and cuSPARSE, respectively. It also achieves 62.3Œ and 11.25Œ energy saving over OuterSPACE and SpArch, respectively.