In real-time system audio translation architectures, passing raw PCM audio frames from the native C++ WASAPI capture loop to secondary inference processes (e.g., Python, ONNX Runtime, or TensorRT wrapper environments) via TCP sockets or standard Named Pipes introduces inter-process communication (IPC) overhead, context switching latency, and high memory allocation churn.

1. Why Standard IPC Fails Low-Latency Windows Requirements

• Named Pipes (CreateNamedPipe): Imposes kernel-mode to user-mode memory copying on every buffer read/write cycle (~2ms–5ms overhead per chunk).
• TCP Loopback (127.0.0.1): Subject to Windows socket stack scheduling and TCP packet buffering jitter.

2. Native Win32 Memory-Mapped Ring Buffer Architecture

Using Win32 Memory-Mapped Files (CreateFileMappingW) paired with atomic memory pointers provides zero-copy shared memory IPC with sub-millisecond frame transfer speeds.

3. Architectural Design:

1. Shared Memory Allocation:
   • Host creates a named shared memory region using CreateFileMappingW(INVALID_HANDLE_VALUE, NULL, PAGE_READWRITE, 0, BUFFER_SIZE, L"Local\\VoxisAudioRingBuffer").
   • Secondary process maps the view using MapViewOfFile.
2. Lock-Free Circular Buffer (Ring Buffer):
   • Structure the memory region with atomic head and tail offsets using std::atomic<uint32_t>.
   • C++ WASAPI thread writes raw PCM frames directly to buffer[head] and updates the atomic pointer using relaxed memory ordering.
3. Synchronization Signals:
   • Use Win32 Event objects (CreateEventW) for signaling.
   • SetEvent(hAudioReady) notifies the inference thread; the consumer thread waits via WaitForSingleObject(hAudioReady, TIMEOUT).

4. Benchmarks

What IPC patterns are you utilizing to bridge native C++ audio capture threads with managed AI inference runtimes?