  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.
