13 citations · 29 across the 14 of their papers we have counts for
4 papers · 1 filter
DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference
Jiajun Zhou, Jiajun Wu, Yizhao Gao +7
To accelerate the inference of deep neural networks (DNNs), quantization with low-bitwidth numbers is actively researched. A prominent challenge is to quantize the DNN models into…
ODG-Q: Robust Quantization via Online Domain Generalization
Chaofan Tao, Ngai Wong
Quantizing neural networks to low-bitwidth is important for model deployment on resource-limited edge hardware. Although a quantized network has a smaller model size and memory foo…
PECAN: A Product-Quantized Content Addressable Memory Network
Jie Ran, Rui Lin, Jason Chun Lok Li +2
A novel deep neural network (DNN) architecture is proposed wherein the filtering and linear transform are realized solely with product quantization (PQ). This results in a natural…
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression
Rui Lin, Ching-Yun Ko, Zhuolun He +5
The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy. In this regard, low-rank tensor decomposition constitut…