Traditional Deep Neural Network (DNN) quantization methods using integer,
fixed-point, or floating-point data types struggle to capture diverse DNN
parameter distributions at low precision, and often require large silicon
overhead and intensive quantization-aware training. In this study, we introduce
Logarithmic Posits (LP), an adaptive, hardware-friendly data type inspired by
posits that dynamically adapts to DNN weight/activation distributions by
parameterizing LP bit fields. We also develop a novel genetic-algorithm based
framework, LP Quantization (LPQ), to find optimal layer-wise LP parameters
while reducing representational divergence between quantized and full-precision
models through a novel global-local contrastive objective. Additionally, we
design a unified mixed-precision LP accelerator (LPA) architecture comprising
of processing elements (PEs) incorporating LP in the computational datapath.
Our algorithm-hardware co-design demonstrates on average <1% drop in top-1
accuracy across various CNN and ViT models. It also achieves ~ 2x improvements
in performance per unit area and 2.2x gains in energy efficiency compared to
state-of-the-art quantization accelerators using different data types.

本研究介绍了一种基于对数正数（LP）的自适应、硬件友好的数据类型，通过参数化 LP 位域，动态适应深度神经网络（DNN）的权重 / 激活分布。我们还开发了一种基于遗传算法的 LP 量化（LPQ）框架，通过新颖的全局 - 局部对比目标减小量化与全精度模型之间的表示差异。此外，我们设计了一个统一的混合精度 LP 加速器（LPA）体系架构，其中计算数据路径中的处理元件（PE）采用 LP。我们的算法 - 硬件协同设计在各种 CNN 和 ViT 模型上实现了平均小于 1% 的 top-1 准确率下降，与使用不同数据类型的最先进量化加速器相比，性能单位面积提高了约 2 倍，并提高了 2.2 倍的能源效率。