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This workMicro-architecture Instruction issue 4-way VLlW 8-way VLlW work A 533MHz 2.5W 2132MIPS 12.8GOPS 2.1GFLOPS 8-way VLlW embedded multimedia processor occupies a 7.8~7.8"~ die in a 7-layer metal 0.1 1 y m CMOS at 1.2V. VLIW, SIMD, dynamic branch prediction, non-aligned dual load/store mechanism and a crosstalk-aware design flow contribute to performance.

Background: To treat diseases caused by genetic variants, it is necessary to identify disease-causing variants in patients. However, since there are a large number of disease-causing variants, the application of AI is required. We propose AI to solve this problem and report the results of its application in identifying disease-causing variants. Methods: To assist physicians in their task of identifying disease-causing variants, we propose an explainable AI (XAI) that combines high estimation accuracy with explainability using a knowledge graph. We integrated databases for genomic medicine and constructed a large knowledge graph that was used to achieve the XAI. Results: We compared our XAI with random forests and decision trees. Conclusion: We propose an XAI that uses knowledge graphs for explanation. The proposed method achieves high estimation performance and explainability. This will support the promotion of genomic medicine.

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