  This paper presents a novel approach to neuromorphic audio processing by
integrating the strengths of Spiking Neural Networks (SNNs), Transformers, and
high-performance computing (HPC) into the HPCNeuroNet architecture. Utilizing
the Intel N-DNS dataset, we demonstrate the system's capability to process
diverse human vocal recordings across multiple languages and noise backgrounds.
The core of our approach lies in the fusion of the temporal dynamics of SNNs
with the attention mechanisms of Transformers, enabling the model to capture
intricate audio patterns and relationships. Our architecture, HPCNeuroNet,
employs the Short-Time Fourier Transform (STFT) for time-frequency
representation, Transformer embeddings for dense vector generation, and SNN
encoding/decoding mechanisms for spike train conversions. The system's
performance is further enhanced by leveraging the computational capabilities of
NVIDIA's GeForce RTX 3060 GPU and Intel's Core i9 12900H CPU. Additionally, we
introduce a hardware implementation on the Xilinx VU37P HBM FPGA platform,
optimizing for energy efficiency and real-time processing. The proposed
accelerator achieves a throughput of 71.11 Giga-Operations Per Second (GOP/s)
with a 3.55 W on-chip power consumption at 100 MHz. The comparison results with
off-the-shelf devices and recent state-of-the-art implementations illustrate
that the proposed accelerator has obvious advantages in terms of energy
efficiency and design flexibility. Through design-space exploration, we provide
insights into optimizing core capacities for audio tasks. Our findings
underscore the transformative potential of integrating SNNs, Transformers, and
HPC for neuromorphic audio processing, setting a new benchmark for future
research and applications.
