MelT: GEMM-Native NDFT for Efficient Single-Stage Audio Frontends on Modern Accelerators

Abstract

Modern audio processing networks are commonly deployed on accelerators whose peak throughput is obtained through dense linear algebra, whereas conventional acoustic frontends -- a Short-Time Fourier Transform (STFT) followed by sparse Mel aggregation -- remain structurally heterogeneous. This mismatch can introduce memory-bandwidth, dispatch, and intermediate-allocation overheads on contemporary accelerator backends. This work introduces MelT, a single-stage frontend framework in which Mel-spaced Non-Uniform Discrete Fourier Transform (NDFT) bases are precomputed and applied to time-domain acoustic frames through dense General Matrix Multiplication (GEMM) operations. The contribution is not the NDFT operator itself; rather, it is the formulation of Mel-spaced NDFT projection as a GEMM-native audio frontend and its evaluation as a hardware-efficient alternative to conventional STFT+Mel pipelines. Evaluated across platforms ranging from Apple A18 Pro edge hardware to NVIDIA H100 datacenter acceleration, MelT attains up to a $3.75\times$ speedup in inference latency and a $3.52\times$ reduction in energy consumption while maintaining downstream classification accuracy.