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PDF Trainable quantization for Speedy Spiking Neural Networks

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190603193 Fighting Quantization Bias With Bias arXivorg

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arXiv200200104v2 csCV 18 Mar 2024


3 mars 2024 · In this article we propose an in-depth characterization of SNN quantization noise. We then propose a end-to-end direct learning approach based on a new trainable spiking neural model. This model allows adapting the threshold of neurons during training and implements efficient quantization strategies. This novel approach better. 1 mars 2024 · Alain Pegatoquet Benoit Miramond View Last Updated: 20 Mar 2024 PDF | Spiking neural networks are considered as the third generation of Artificial Neural Networks. SNNs perform. end direct learning approach based on a new trainable spiking neural model. This model allows adapting the threshold of neurons during training and implements ecient quantization. Our method shows the possibility of achieving an accuracy higher than 70% on ImageNet with spiking neurons in the output layer. 3. Motivation. The motivation for this research is to develop a practical method to reconcile the accuracy-latency trade-off in SNNs. Weighted-Entropy-Based Quantization for Deep Neural Networks. Abstract: Quantization is considered as one of the most effective methods to optimize the inference cost of neural network models for their deployment to mobile and embedded systems, which have tight resource constraints. We obtain the representative weight value rk for clus-ter Ck (line 14). In order to identify which weights belong to which cluster, weight values at cluster boundaries, bk, are identified as well for weight quantization (line 15), i.e., cluster Ci contains weights w that satisfy bk ≤ w < bk+1. 1 juil. 2017 · Weighted-Entropy-Based Quantization for Deep Neural Networks. This paper proposes a novel method for quantizing weights and activations based on the concept of weighted entropy, which achieves significant reductions in both the model size and the amount of computation with minimal accuracy loss. 1 juil. 2017 · Download Citation | On Jul 1, 2017, Eunhyeok Park and others published Weighted-Entropy-Based Quantization for Deep Neural Networks | Find, read and cite all the research you need on ResearchGate. In this paper, we propose a novel method for quantizing weights and activations based on the concept of weighted entropy. Unlike recent work on binary-weight neural networks, our approach is multi-bit quantization, in which weights and activations can be quantized by any number of bits depending on the target accuracy. Figure 2. Top-1 and top-5 accuracy of quantized CNNs after finetuning. The dashed lines represent the accuracy of the baseline networks, which use full-precision arithmetic. - "Weighted-Entropy-Based Quantization for Deep Neural Networks".