Real-Time Acoustic Anomaly Detection on Cortex-M4: Fixed-Point STFT and INT8 TFLM in Under 64KB SRAM

Deploying predictive maintenance on low-power industrial edge devices often demands running continuous acoustic anomaly detection on deeply constrained silicon. When targeting a bare-metal ARM Cortex-M4 with only 64KB of SRAM and no operating system, developers cannot afford floating-point inefficiencies, dynamic heap allocation, or large spectrogram buffers. Achieving real-time performance requires a deterministic pipeline that tightly couples hardware peripherals, hand-tuned DSP routines, and aggressive model quantization.

Zero-Copy Audio Acquisition and Fixed-Point STFT

The signal chain starts with a digital I2S MEMS microphone streaming PCM samples directly into memory via circular DMA. By using a ping-pong buffer topology, the CPU processes the previous audio window while the DMA controller gathers the next frame without audio dropouts. Rather than relying on power-hungry floating-point routines, spectral extraction utilizes CMSIS-DSP's arm_rfft_q15. Converting the raw 16-bit audio into a log-mel spectrogram using fixed-point Q15 arithmetic maintains numerical precision while avoiding saturation via strategic bit-shifts. This reduces the feature extraction time to just a few milliseconds per 32ms audio frame.

Squeezing the TFLite Micro Arena into 64KB SRAM

In a 64KB SRAM system, memory partitioning leaves zero room for fragmentation. The system RAM must host the stack, interrupt vectors, DMA buffers, and the TensorFlow Lite for Microcontrollers (TFLM) tensor arena. We can structure our allocations predictably by avoiding standard library heap calls entirely:

• Audio Buffers: Double-buffered 512-sample Q15 ping-pong buffers consuming roughly 2KB.
• Feature Matrices: A rolling spectrogram cache storing temporal context (e.g., 32 frequency bins across 16 time steps) requiring under 1KB.
• TFLM Tensor Arena: A statically declared uint8_t buffer capped at 36KB to house model weights, intermediate activation tensors, and operator scratchpads.
• Stack and Global State: The remaining ~25KB allocated safely for system execution and interrupt stacks.

Executing the Quantized INT8 Autoencoder

For anomaly detection, an INT8 fully quantized deep autoencoder or compact 1D convolutional network serves as the core inference engine. Trained on nominal operational audio (such as pump or motor whirs), the model reconstructs the input spectrogram. At runtime on the Cortex-M4, CMSIS-NN optimized kernels execute the quantized matrix multiplications using single-cycle SIMD instructions like SMLAD. An anomaly score is computed by calculating the Mean Squared Error (MSE) between the input INT8 features and the reconstructed output directly in fixed-point space. Exceeding a predetermined threshold flags potential mechanical failure, such as bearing wear or cavitation.

Deterministic Optimization and Cycle Budgets

Running bare-metal ensures absolute determinism: there is no RTOS scheduler jitter or thread preemption overhead. At an 80MHz clock rate, an end-to-end cycle—consisting of DMA handling, Q15 STFT calculation, log-magnitude scaling, and INT8 model inference—executes in approximately 18ms, comfortably fitting within a 32ms hop size. This yields a 40% duty cycle margin, allowing the Cortex-M4 to enter low-power sleep modes between windows to conserve energy in battery-operated or energy-harvesting industrial sensor nodes.