Large language models display three universal scale-dependent regimes of behavior—stable, chaotic, and signal-dominated—driven by floating-point rounding errors that produce an avalanche effect in early layers.
Smoothquant: Accurate and efficient post-training quantization for large language models,
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TREA is a low-precision time-multiplexed edge accelerator using dual-precision SIMD MAC units, structured pruning, and reconfigurable activation cores to deliver up to 9x kernel-level latency reduction for object detection and classification.
citing papers explorer
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Numerical Instability and Chaos: Quantifying the Unpredictability of Large Language Models
Large language models display three universal scale-dependent regimes of behavior—stable, chaotic, and signal-dominated—driven by floating-point rounding errors that produce an avalanche effect in early layers.
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TREA: Low-precision Time-Multiplexed, Resource-Efficient Edge Accelerator for Object Detection and Classification
TREA is a low-precision time-multiplexed edge accelerator using dual-precision SIMD MAC units, structured pruning, and reconfigurable activation cores to deliver up to 9x kernel-level latency reduction for object detection and classification.