RISC-V–based processors and ML accelerators are increasingly targeted for latency-sensitive domains such as automotive Software-Defined Vehicle platforms and edge systems, where runtime observability is essential for performance validation and early fault diagnosis. Although RISC-V standardizes architectural and hardware performance monitoring counters, raw cumulative snapshots do not directly provide window-level deltas or streaming metrics required for real-time analytics. To bridge this gap, we present a monitoring tool that implements a window-level telemetry pipeline to enable real-time observability. It converts cumulative counters into per-window delta values, selects a curated metric set, and computes derived metrics. The resulting telemetry is recorded simultaneously as CSV and structured logs (NDJSON) and streamed to external consumers via ZeroMQ for runtime processing. The approach is validated using a cycle-level gem5 RISC-V simulation, demonstrating 2–3 ms host-side processing per 10 ms window with minimal overhead. The modular design incorporates a source-agnostic acquisition layer, allowing the input backend to be replaced by hardware performance counters with minimal changes to the core processing and output interfaces.