A progressive training scheme with binary-aware initialization and dual-scaling allows pre-trained LLMs to be converted to high-performance 1-bit models without training from scratch.
Incremental network quantization: Towards lossless cnns with low-precision weights
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
This paper presents incremental network quantization (INQ), a novel method, targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model into a low-precision version whose weights are constrained to be either powers of two or zero. Unlike existing methods which are struggled in noticeable accuracy loss, our INQ has the potential to resolve this issue, as benefiting from two innovations. On one hand, we introduce three interdependent operations, namely weight partition, group-wise quantization and re-training. A well-proven measure is employed to divide the weights in each layer of a pre-trained CNN model into two disjoint groups. The weights in the first group are responsible to form a low-precision base, thus they are quantized by a variable-length encoding method. The weights in the other group are responsible to compensate for the accuracy loss from the quantization, thus they are the ones to be re-trained. On the other hand, these three operations are repeated on the latest re-trained group in an iterative manner until all the weights are converted into low-precision ones, acting as an incremental network quantization and accuracy enhancement procedure. Extensive experiments on the ImageNet classification task using almost all known deep CNN architectures including AlexNet, VGG-16, GoogleNet and ResNets well testify the efficacy of the proposed method. Specifically, at 5-bit quantization, our models have improved accuracy than the 32-bit floating-point references. Taking ResNet-18 as an example, we further show that our quantized models with 4-bit, 3-bit and 2-bit ternary weights have improved or very similar accuracy against its 32-bit floating-point baseline. Besides, impressive results with the combination of network pruning and INQ are also reported. The code is available at https://github.com/Zhouaojun/Incremental-Network-Quantization.
representative citing papers
16-bit log-based training achieves classification accuracy within approximately 1% of floating-point baselines on common datasets by replacing multiplications with approximate log-domain additions.
TAC framework separates optimization of convolutional and fully connected layers in 1-bit DNNs to improve accuracy while maintaining efficiency.
The prune-quantize-distill ordering produces a better accuracy-size-latency frontier on CIFAR-10/100 than any single technique or other orderings, with INT8 QAT providing the main runtime gain.
citing papers explorer
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Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
A progressive training scheme with binary-aware initialization and dual-scaling allows pre-trained LLMs to be converted to high-performance 1-bit models without training from scratch.
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Neural Network Training with Approximate Logarithmic Computations
16-bit log-based training achieves classification accuracy within approximately 1% of floating-point baselines on common datasets by replacing multiplications with approximate log-domain additions.
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A Targeted Acceleration and Compression Framework for Low bit Neural Networks
TAC framework separates optimization of convolutional and fully connected layers in 1-bit DNNs to improve accuracy while maintaining efficiency.
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Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression
The prune-quantize-distill ordering produces a better accuracy-size-latency frontier on CIFAR-10/100 than any single technique or other orderings, with INT8 QAT providing the main runtime gain.