提出高效前端框架，让边缘设备实时处理32路关键词识别且功耗降低六成。
ASAP-FE: Energy-Efficient Feature Extraction Enabling Multi-Channel Keyword Spotting on Edge Processors
- 用半重叠IIR分帧减少25%冗余数据，保留语音过渡特征。
- 结合跳帧与步长滤波，帧级稀疏性使数据量再降50%。
- 动态并行处理机制支持多通道实时运行，适合低功耗边缘场景。
多通道关键词识别在边缘语音应用中日益重要，但其高计算与能耗带来挑战。本文提出硬件友好的前段框架ASAP-FE（敏捷稀疏感知并行特征提取器），包含三项创新：(1) 半重叠无限冲激响应（IIR）分帧，减少约25%冗余数据，同时保留关键音素过渡信息；(2) 帧级稀疏感知数据压缩，通过跳帧与步长滤波实现额外50%数据缩减；(3) 可配置滤波簇与优先级调度算法，支持IIR任务并行执行，降低延迟并优化能效。ASAP-FE在多种滤波簇规模下于边缘处理器实现，经FPGA原型验证及45nm工艺综合。基于TC-ResNet8、DS-CNN和KWT-1的实验表明，该方案平均工作负载降低62.73%，支持最多32路通道实时处理；相比全重叠基线，准确率下降不足1%（如DS-CNN为96.22% vs. 97.13%），处于可接受范围。通过调整滤波模块数，可在性能与能效间权衡，15个并行滤波器对最多25路通道表现最优。整体上，ASAP-FE为资源受限边缘设备提供了实用高效的多通道关键词识别解决方案。
原文摘要 · Abstract (English)
Multi-channel keyword spotting (KWS) has become crucial for voice-based applications in edge environments. However, its substantial computational and energy requirements pose significant challenges. We introduce ASAP-FE (Agile Sparsity-Aware Parallelized-Feature Extractor), a hardware-oriented front-end designed to address these challenges. Our framework incorporates three key innovations: (1) Half-overlapped Infinite Impulse Response (IIR) Framing: This reduces redundant data by approximately 25% while maintaining essential phoneme transition cues. (2) Sparsity-aware Data Reduction: We exploit frame-level sparsity to achieve an additional 50% data reduction by combining frame skipping with stride-based filtering. (3) Dynamic Parallel Processing: We introduce a parameterizable filter cluster and a priority-based scheduling algorithm that allows parallel execution of IIR filtering tasks, reducing latency and optimizing energy efficiency. ASAP-FE is implemented with various filter cluster sizes on edge processors, with functionality verified on FPGA prototypes and designs synthesized at 45 nm. Experimental results using TC-ResNet8, DS-CNN, and KWT-1 demonstrate that ASAP-FE reduces the average workload by 62.73% while supporting real-time processing for up to 32 channels. Compared to a conventional fully overlapped baseline, ASAP-FE achieves less than a 1% accuracy drop (e.g., 96.22% vs. 97.13% for DS-CNN), which is well within acceptable limits for edge AI. By adjusting the number of filter modules, our design optimizes the trade-off between performance and energy, with 15 parallel filters providing optimal performance for up to 25 channels. Overall, ASAP-FE offers a practical and efficient solution for multi-channel KWS on energy-constrained edge devices.
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