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Found 13 papers, 7 papers with code
ML2SC: Deploying Machine Learning Models as Smart Contracts on the Blockchain
Finally, the model inference can also be done with a function call providing the input.
LLM Inference Unveiled: Survey and Roofline Model Insights
Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model for systematic analysis of LLM inference techniques.
RepQuant: Towards Accurate Post-Training Quantization of Large Transformer Models via Scale Reparameterization
In particular, for the former, we introduce a learnable per-channel dual clipping scheme, which is designed to efficiently identify outliers in the unbalanced activations with fine granularity.
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction
This paper presents a unique solution to challenges in medical image processing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation.
RTA-Former: Reverse Transformer Attention for Polyp Segmentation
Polyp segmentation is a key aspect of colorectal cancer prevention, enabling early detection and guiding subsequent treatments.
Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models
Specifically, at the calibration sample level, we select calibration samples based on the density and diversity in the latent space, thus facilitating the alignment of their distribution with the overall samples; and at the reconstruction output level, we propose Fine-grained Block Reconstruction, which can align the outputs of the quantized model and the full-precision model at different network granularity.
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources
Large Language Models (LLMs) have showcased remarkable impacts across a wide spectrum of natural language processing tasks.
BinaryViT: Towards Efficient and Accurate Binary Vision Transformers
In this paper, we first argue empirically that the severe performance degradation is mainly caused by the weight oscillation in the binarization training and the information distortion in the activation of ViTs.
Patch-wise Mixed-Precision Quantization of Vision Transformer
As emerging hardware begins to support mixed bit-width arithmetic computation, mixed-precision quantization is widely used to reduce the complexity of neural networks.
RepQ-ViT: Scale Reparameterization for Post-Training Quantization of Vision Transformers
Post-training quantization (PTQ), which only requires a tiny dataset for calibration without end-to-end retraining, is a light and practical model compression technique.
PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers
In this paper, we propose PSAQ-ViT V2, a more accurate and general data-free quantization framework for ViTs, built on top of PSAQ-ViT.
I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference
In this paper, we propose I-ViT, an integer-only quantization scheme for ViTs, to enable ViTs to perform the entire computational graph of inference with integer arithmetic and bit-shifting, and without any floating-point arithmetic.
Patch Similarity Aware Data-Free Quantization for Vision Transformers
The above insights guide us to design a relative value metric to optimize the Gaussian noise to approximate the real images, which are then utilized to calibrate the quantization parameters.
