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I-Parakeet represents a significant advancement in edge AI by successfully deploying a 0.6B parameter Conformer ASR model entirely on integer-only NPUs, eliminating the need for floating-point fallbacks or CPU offloading. The research achieves this through novel integer formulations of self-attention mechanisms and optimized activation approximations, resulting in real-time inference speeds that are 7.5x faster than standard CPU baselines. This work directly addresses the deployment bottleneck of modern speech models on mobile hardware by fully leveraging specialized integer accelerators.
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I-Parakeet: Integer-Only Conformer ASR on Mobile NPU

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arXiv:2609.30846v1 Announce Type: new Abstract: In this paper, we propose I-Parakeet, an integer-only implementation of NVIDIA's Parakeet-CTC (0.6B parameters) that runs on a smartphone NPU without any floating-point operator or CPU fallback. Modern Conformer ASR models are hard to deploy on edge devices because of their size, and quantized models still fall back to floating point for numerically sensitive operations. This prevents them from fully exploiting integer accelerators such as mobile NPUs. To achieve this, our contributions are threefold. First, we derive an integer formulation of the relative-positional self-attention at the core of the Conformer. We fuse its two score branches with different quantization scales and the relative shift into integer-only operations. Second, we introduce a minimax-optimized Swish approximation that minimizes the maximum error of the Swish output. Third, a layer-wise range analysis of activations yields two targeted remedies: an INT16 grid for the BatchNorm output and percentile calibration for the heavy-tailed pre-encoder activations. I-Parakeet achieves 4.97% WER on LibriSpeech test-other, running on a Qualcomm NPU at a real-time factor of 0.048, 7.5x faster than a CPU baseline.

Abstract:In this paper, we propose I-Parakeet, an integer-only implementation of NVIDIA's Parakeet-CTC (0.6B parameters) that runs on a smartphone NPU without any floating-point operator or CPU fallback. Modern Conformer ASR models are hard to deploy on edge devices because of their size, and quantized models still fall back to floating point for numerically sensitive operations. This prevents them from fully exploiting integer accelerators such as mobile NPUs. To achieve this, our contributions are threefold. First, we derive an integer formulation of the relative-positional self-attention at the core of the Conformer. We fuse its two score branches with different quantization scales and the relative shift into integer-only operations. Second, we introduce a minimax-optimized Swish approximation that minimizes the maximum error of the Swish output. Third, a layer-wise range analysis of activations yields two targeted remedies: an INT16 grid for the BatchNorm output and percentile calibration for the heavy-tailed pre-encoder activations. I-Parakeet achieves 4.97% WER on LibriSpeech test-other, running on a Qualcomm NPU at a real-time factor of 0.048, 7.5x faster than a CPU baseline.

Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)

arXiv:2609.30846 [cs.CL]

(or arXiv:2609.30846v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.30846 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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