June 2026 monthly summary: The primary focus was feature delivery to strengthen Hexagon backend performance for neural network operations. Added GELU Quick activation support in the Hexagon backend for both repositories ggml-org/llama.cpp and ggml-org/ggml, enabling faster inference on Hexagon hardware. No major bugs were reported this month; the work emphasized cross-repo consistency and capability expansion, establishing a foundation for higher performance on edge devices.

May 2026 monthly summary: Delivered performance-oriented updates for Hexagon-backed ssm-conv across ggml and llama.cpp, with a focus on large-prompt workloads. Implemented substantial performance improvements for the ssm-conv operation in the Hexagon backend and expanded test coverage to validate behavior under realistic usage. Addressed a critical ssm-conv bug in the Hexagon backend, followed by additional optimizations to reduce gating and improve stability. Synced changes across repositories ggml-org/ggml and ggml-org/llama.cpp to ensure consistency and maintainability. Result: faster, more reliable inference for large prompts, improved test resilience, and stronger readiness for scale. Skills demonstrated: C/C++ performance tuning, test-driven development, cross-repo collaboration, and Hexagon backend optimization.

April 2026: Delivered Hexagon-accelerated tensor operations and cross-platform readiness across ggml and llama.cpp, focusing on business value, performance, and developer experience. Key outcomes include faster cumulative sum workloads on Hexagon, broader Linux-on-Snapdragon support, and improved onboarding and build clarity for multi-repo deployments.

March 2026 performance snapshot: Delivered Hexagon-optimized ML execution enhancements for llama.cpp and ggml, enabling more efficient deployment and inference on Hexagon devices. Implemented a new PowerShell runner for llama-completion on Hexagon to streamline execution and configuration management. Introduced Hexagon backend ssm_conv with dynamic scratchpad, HVX optimizations, FP32 support, and DMA data handling, expanding capabilities and performance. Added f32 ssm_conv support in the ggml Hexagon backend with HVX optimizations and scratchpad improvements. Addressed kernel reliability by fixing scratchpad indexing and improving dynamic scratchpad computation. These workstreams collectively improved throughput, reduced latency, and broadened deployment options across supported devices, with cross-repo collaboration (co-authored-by Max Krasnyansky).

Month: 2026-01 — This monthly summary highlights key deliverables and business value from ggml-org/ggml and ggml-org/llama.cpp, focusing on enabling Hexagon DSP offload on Windows for Snapdragon devices, and the associated build, driver interface, and documentation improvements that established a solid, maintainable foundation for hardware-accelerated AI workloads. Overall, the work accelerates AI inference on Windows-equipped Snapdragon hardware, improves power efficiency, and strengthens cross-repo parity and maintainability through refactors and build-system enhancements.