5 accepted papers
The emergence of accurate open large language models (LLMs) has sparked a push for advanced quantization techniques to enable efficient deployment on end-user devices. In this paper, we revisit the challenge of extreme LLM compression---targeting ultra-low-bit quantization for both activations and w
The rapid advancement of large language models (LLMs) has exacerbated the memory bottleneck due to the widening gap between model parameter scaling and hardware capabilities. While post-training quantization techniques effectively reduce memory overhead, existing methods predominantly rely on static
Current Internet of Things (IoT) embedded applications use machine learning algorithms to process the collected data. However, the computational complexity and storage requirements of existing deep learning methods hinder the wide availability of embedded applications. Spiking Neural Networks (SNN)…
Spiking Neural Networks (SNNs) have recently attracted enormous research interest since their event-driven and brain-inspired structure enables low-power computation. In image recognition tasks, the best results are achieved by SNN so far utilizing ANN-SNN conversion methods that replace activation…
Model quantization has emerged as a mandatory technique for efficient inference with advanced Deep Neural Networks (DNN). It converts the model parameters in full precision (32-bit floating point) to the hardware friendly data representation with shorter bit-width, to not only reduce the model size…