针对边缘设备设计自适应异构量化方法，高效平衡精度与资源消耗。
SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference
- 按层分配不同比特位宽，动态适配各层鲁棒性差异。
- 无需暴力搜索，快速生成满足多约束的量化方案。
- 适合资源受限的移动端或嵌入式部署场景。
深度神经网络（DNN）在边缘或移动设备上执行复杂任务至关重要，但其部署常受内存、功耗和算力限制。统一量化虽简单易行，却未能充分利用各层对量化不同的容忍度，导致低比特下精度下降或资源利用不充分。异构量化通过为各层分配不同比特位宽可缓解此问题，但现有方法或需大量穷举搜索，或难以适应不同的硬件条件（如内存大小、能耗预算、延迟要求）。为此，本文提出 extbf{ extit{SigmaQuant}}，一种自适应的逐层异构量化框架，可在无需遍历设计空间的前提下，高效平衡不同边缘环境下的模型精度与资源使用。
原文摘要 · Abstract (English)
Deep neural networks (DNNs) are essential for performing advanced tasks on edge or mobile devices, yet their deployment is often hindered by severe resource constraints, including limited memory, energy, and computational power. While uniform quantization provides a straightforward approach to compress model and reduce hardware requirement, it fails to fully leverage the varying robustness across layers, and often lead to accuracy degradation or suboptimal resource usage, particularly at low bitwidths. In contrast, heterogeneous quantization, which allocates different bitwidths to individual layers, can mitigate these drawbacks. Nonetheless, current heterogeneous quantization methods either needs huge brute-force design space search or lacks the adaptability to meet different hardware conditions, such as memory size, energy budget, and latency requirement. Filling these gaps, this work introduces \textbf{\textit{SigmaQuant}}, an adaptive layer-wise heterogeneous quantization framework designed to efficiently balance accuracy and resource usage for varied edge environments without exhaustive search.
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