Summary for 2026-06: Focused on performance optimization of fused MoE path in FlagGems, delivering substantial throughput and latency improvements for W4A16 FP4/FP8 paths across Hopper-based hardware, along with code quality and maintainability gains that streamline future optimizations.

May 2026: Delivered a block-wise FP8 einsum acceleration for tensor contractions in FlagGems, with a dedicated FP8 BMM kernel (bhr,hdr->bhd). Added comprehensive tests validating FP8 accuracy and performance against FP16/FP32 baselines. No major bugs fixed this month. Impact: improved throughput and memory efficiency for ML workloads on FP8-capable hardware, enabling faster tensor operations. Technologies demonstrated: FP8, block-wise algorithms, tensor contractions, end-to-end testing, benchmarking.

Month: 2026-04 — Focused on performance optimization for fused experts in FlagOpen/FlagGems. Delivered blockwise FP8 W8A8 optimization to enhance training and inference efficiency, reducing resource usage and improving throughput in large-scale model deployments.

November 2024: Delivered a Dockerized cross-platform build and development workflow for arceos, enabling reproducible multi-arch builds (aarch64, riscv64, x86_64) via a Rust-focused Dockerfile. Updated README with Docker build/run instructions and reorganized docs to reflect the new workflow. This work streamlines onboarding, accelerates local development, and strengthens CI reliability for cross-arch targets.