{"slug": "nanq-noise-floor-aware-mixed-precision-non-uniform-quantization-for-analog-in", "title": "NANQ: Noise-Floor-Aware Mixed-Precision Non-Uniform Quantization for Analog Compute-in-Memory", "summary": "Researchers propose NANQ, a noise-aware mixed-precision non-uniform quantization framework for analog compute-in-memory (CIM), which models magnitude-dependent weight noise from measured eFlash CIM arrays to assign finer resolution to low-noise regions. On-chip experiments on an eFlash CIM SoC show that under 2-bit weight-magnitude quantization, NANQ improves vision-model accuracy by 8.05 percentage points and reduces language-model perplexity by 54.7% on average over PowerQuant, with mixed-precision NANQ capturing most gains using only 3.2-3.8 equivalent bits.", "body_md": "arXiv:2608.02700v1 Announce Type: new\nAbstract: Analog compute-in-memory (CIM) enables energy-efficient neural network inference, but device variation and read noise can severely degrade low-bit quantized models. Existing CIM-oriented quantization methods mainly minimize ideal quantization error, ignoring the hardware noise floor and thus causing inefficient precision allocation. We propose NANQ, a noise-aware mixed-precision non-uniform quantization framework for analog CIM. NANQ models magnitude-dependent weight noise from measured responses of an eFlash CIM array and converts the noise profile into an adaptive quantization density, assigning finer resolution to low-noise regions while avoiding ineffective precision in noise-dominated regions. It further assigns layer-wise bit-widths by identifying each layer's precision saturation point under hardware noise using a unified threshold. On-chip experiments on an eFlash CIM SoC show that, under 2-bit weight-magnitude quantization, NANQ improves vision-model accuracy by 8.05 percentage points and reduces language-model PPL by 54.7% on average over PowerQuant. Mixed-precision NANQ captures most of the gains obtainable from additional quantization resources with only 3.2-3.8 equivalent bits.", "url": "https://wpnews.pro/news/nanq-noise-floor-aware-mixed-precision-non-uniform-quantization-for-analog-in", "canonical_source": "https://arxiv.org/abs/2608.02700", "published_at": "2026-08-05 04:00:00+00:00", "updated_at": "2026-08-05 04:02:18.816082+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "ai-infrastructure"], "entities": ["NANQ", "PowerQuant", "eFlash CIM"], "alternates": {"html": "https://wpnews.pro/news/nanq-noise-floor-aware-mixed-precision-non-uniform-quantization-for-analog-in", "markdown": "https://wpnews.pro/news/nanq-noise-floor-aware-mixed-precision-non-uniform-quantization-for-analog-in.md", "text": "https://wpnews.pro/news/nanq-noise-floor-aware-mixed-precision-non-uniform-quantization-for-analog-in.txt", "jsonld": "https://wpnews.pro/news/nanq-noise-floor-aware-mixed-precision-non-uniform-quantization-for-analog-in.jsonld"}}