Month: 2026-07 — Jeejeelee/vllm. Highlights include two key deliverables and multiple reliability improvements that drive business value. 1) Arm-optimized GELU activation for ARM CPUs: a high-performance tanh-based GELU kernel using Arm Optimized Routines (AOR), with new C++ kernel implementations, Torch bindings, and updated Python layer logic to accelerate GELU within vLLM (commit 89e99202f285d1dc1168f87f6cc3e26e930970ff). 2) Re-enable LibriSpeech benchmark dataset: updated ASRDataset configuration and loading flow; unit tests expanded to support file-based and byte-based audio inputs; dataset is registered and processed during benchmarks (commit d1f1d86797ad2f4cccfe441451ba320583c87cd2). 3) Major bugs fixed: bugfix to re-enable LibriSpeech benchmarking, improving stability and reproducibility of benchmark runs. 4) Overall impact: significant ARM performance enhancements for GELU-heavy workloads; expanded, stable benchmarking coverage enabling better performance regression tracking and platform comparisons. 5) Technologies/skills demonstrated: C++ kernel development, Torch bindings, Python integration, ARM Optimized Routines, dataset configuration and test automation, cross-team collaboration and code quality.

May 2026 (madeline-underwood/arm-learning-paths) focused on delivering quantized-inference improvements and documentation improvements to enable faster, more reliable deployment decisions. Key work includes: 1) Inference benchmarking enhancements for quantized models (Llama and Whisper) with an updated inference page that uses custom scripts and now reports full benchmark runs, enabling more accurate speed and accuracy comparisons. 2) Documentation improvements for vLLM installation and usage, plus clarifications on INT8 vs BF16 accuracy for quantized models, improving developer onboarding and deployment guidance. 3) Overall impact: improved benchmarking reliability, clearer guidance for quantized deployments, and faster iteration cycles. Technologies/skills demonstrated include quantized inference, Llama/Whisper, vLLM, INT8/BF16, scripting for benchmarking, and documentation literacy.

April 2026: Delivered a reproducible VLLM benchmarking environment for quantized models in madeline-underwood/arm-learning-paths, including Docker-based development setup, CI workflows for automated testing and deployment, and updated documentation with stability measures for quantisation. Locked quantisation library versions to ensure deterministic results and added thorough w8a8 guidance. This work creates a scalable baseline for ARM-oriented benchmarking, accelerating validation and deployment decisions.

February 2026: Implemented the Whisper CPU Test Spawn Fix in jeejeelee/vllm to stabilize the Whisper CPU test, improving reliability of the testing framework and accuracy of performance evaluations. The fix reduces test flakiness and ensures consistent benchmarking results, enabling more reliable release decisions.

Month: 2025-09. Delivered stateless ACL execution improvements for aarch64 in oneDNN, enabling stateless inner product and fully connected operations via CpuFullyConnected, along with refactoring of ACL inner product for improved resource management. Also refactored lowp matmul static quantization to stateless operations with better memory handling. These changes enhance portability, reduce memory footprint, and pave the way for scalable deployment on aarch64.