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检索条件"主题词=Inference algorithms"

共 5375 条记录，以下是151-160 订阅

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Panacea: Novel DNN Accelerator using Accuracy-Preserving Asymmetric Quantization and Energy-Saving Bit-Slice Sparsity

Panacea: Novel DNN Accelerator using Accuracy-Preserving Asy...

引用

IEEE Symposium on High-Performance Computer Architecture

作者： Dongyun Kam Myeongji Yun Sunwoo Yoo Seungwoo Hong Zhengya Zhang Youngjoo Lee Pohang University of Science and Technology (POSTECH) University of Michigan

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Low bit-precisions and their bit-slice sparsity have recently been studied to accelerate general matrix-multiplications (GEMM) during large-scale deep neural network (DNN) inferences. While the conventional symmetric quantization facilitates low-resolution processing with bit-slice sparsity for both weight and activation, its accuracy loss caused by the activation’s asymmetric distributions cannot be acceptable, especially for largescale DNNs. In efforts to mitigate this accuracy loss, recent studies have actively utilized asymmetric quantization for activations without requiring additional operations. However, the cuttingedge asymmetric quantization produces numerous nonzero slices that cannot be compressed and skipped by recent bit-slice GEMM accelerators, naturally consuming more processing energy to handle the quantized DNN *** simultaneously achieve high accuracy and hardware efficiency for large-scale DNN inferences, this paper proposes an Asymmetrically-Quantized bit-Slice GEMM (AQS-GEMM) for the first time. In contrast to the previous bit-slice computing, which only skips operations of zero slices, the AQS-GEMM compresses frequent nonzero slices, generated by asymmetric quantization, and skips their operations. To increase the slicelevel sparsity of activations, we also introduce two algorithm-hardware co-optimization methods: a zero-point manipulation and a distribution-based bit-slicing. To support the proposed AQS-GEMM and optimizations at the hardware-level, we newly introduce a DNN accelerator, Panacea, which efficiently handles sparse/dense workloads of the tiled AQS-GEMM to increase data reuse and utilization. Panacea supports a specialized dataflow and run-length encoding to maximize data reuse and minimize external memory accesses, significantly improving its hardware efficiency. Numerous benchmark evaluations show that Panacea outperforms existing DNN accelerators, e.g., $1.97 \times$ and $3.26 \times$ higher energy efficiency, and $1.88 \tim

关键词： Quantization (signal Accuracy Symmetric matrices Single instruction multiple data Memory management Artificial neural networks Throughput Hardware inference algorithms Optimization

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MPTorch-FPGA: A Custom Mixed-Precision Framework for FPGA-Based DNN Training

MPTorch-FPGA: A Custom Mixed-Precision Framework for FPGA-Ba...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Sami Ben Ali Silviu-Ioan Filip Olivier Sentieys Guy Lemieux Inria Univ. Rennes University of British Columbia

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Training Deep Neural Networks (DNNs) is computationally demanding, leading to a growing interest in reduced precision formats to enhance hardware efficiency. Several frame-works explore custom number formats with parameterizable precision through software emulation on CPUs or GPUs. However, they lack comprehensive support for different rounding modes and struggle to accurately evaluate the impact of custom precision for FPGA-based targets. This paper introduces MPTorch-FPGA, an extension of the MPTorch framework for performing custom, multi-precision inference and training computations in CPU, GPU, and FPGA environments in PyTorch. MPTorch-FPGA can generate a model-specific accelerator for DNN training, with customizable sizes and arithmetic implementations, providing bit-level accuracy with respect to emulated low precision DNN training on GPUs or CPUs. An offline matching algorithm selects one of several pre-generated (static) FPGA configurations using a custom performance model to estimate latency. To showcase the versatility of MPTorch-FPGA, we present a series of training benchmarks using diverse DNN models, exploring a range of number format configurations and rounding modes. We report both accuracy and hardware performance metrics, verifying the precision of our performance model by comparing estimated and measured latencies across multiple benchmarks. These results highlight the flexibility and practical value of our framework.

关键词： Training Accuracy Emulation Artificial neural networks Benchmark testing Hardware Software inference algorithms Field programmable gate arrays Arithmetic

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Late Breaking Results: Hyperdimensional Regression with Fine-Grained and Scalable Confidence-Based Learning

Late Breaking Results: Hyperdimensional Regression with Fine...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Jiseung Kim Hyunsei Lee Tajana Rosing Mohsen Imani Yeseong Kim DGIST University of California San Diego University of California Irvine

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

We propose an advanced hyperdimensional computing (HDC) framework for regression tasks, addressing the limitations of existing methods through three key innovations: fine-grained feature encoding, confidence-based inference, and dimension-split boosting for scalable training. By preserving inter-feature relationships and enabling efficient computation on high-dimensional spaces, the framework achieves superior accuracy and efficiency across diverse benchmarks. Our evaluation demon-strates that HB R F achieves significant improvements in prediction quality and computational efficiency as compared to the state-of-the-art HDC- based regression by 31% and 54.8 %, respectively.

关键词： Training Technological innovation Accuracy Graphics processing units Boosting Robustness inference algorithms Hardware Computational efficiency Field programmable gate arrays

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Membership Function Shape Optimization of Fuzzy PIDN Controller Based on CTO to Track Output Power of PWR System

Membership Function Shape Optimization of Fuzzy PIDN Control...

引用

Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Sulakshan Naha Dushmanta Kumar Das Department of EEE NIT Nagaland Dimapur Nagaland India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

The shape of fuzzy membership function (MF) is important for performance analysis of any fuzzy inference system (FIS). Normally MFs are chosen by engineering knowledge and expertise but this may not reflect optimal performance in control problems. The aim of the paper is to present a method by using class topper optimization (CTO) algorithm to optimize the shape of membership function of FIS for PWR system. The method is basically to choose the correct membership function for the fuzzy PIDN controller to track output power of PWR system. The simulation result reflects good tracking performance in the proposed work.

关键词： Knowledge engineering Fuzzy logic Shape Simulation inference algorithms Performance analysis Power generation Optimization

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Machine Learning-Driven Binocular Vision Optimization for Enhanced 3D Reconstruction of IP Images

Machine Learning-Driven Binocular Vision Optimization for En...

引用

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Zhouyang Dongfang Chen Hang Xiamen Institute of Technology School of Arts and Communication Xiamen China LUOHE VOCATIONAL TECHNOLOGY COLLEGE HENAN China

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

As an important branch of machine learning, 3D reconstruction algorithms are widely used in visual reconstruction tasks. To overcome the limitations of traditional 3D reconstruction methods in terms of parameter set, inference speed and accuracy, this study proposes a new 3D reconstruction model based on binocular vision optimization algorithm. As a specific scenario, the algorithm is applied to the processing of complex scenes in IP images. The proposed model first combines the prior knowledge of image shape and models it by elliptical feature point sampling and grid multi-homography estimation. This operation can achieve efficient disparity computation; then, the study uses deep learning to optimize the binocular matching algorithm. The algorithm accurately predicts the disparity value through the stacked cost volume construction technology. At the same time, as an algorithm optimization, this study optimizes the reconstruction algorithm based on the optimization strategy of PatchMatch and further reduces the computational complexity. The experiment uses a self-constructed image dataset to verify the performance of the model in various scenarios. The experiment shows that the proposed algorithm has significant advantages in the accuracy and efficiency of 3D reconstruction.

关键词： Solid modeling Three-dimensional displays Machine learning algorithms Accuracy Shape Computational modeling Prediction algorithms inference algorithms IP networks Optimization

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Research on Anomaly Detection Algorithm for Course Assessment Results Based on Data Mining

Research on Anomaly Detection Algorithm for Course Assessmen...

引用

Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Jingjing Dong Yiqiang Bi Jing Liu Xiaoming Wang Fangbing Man Linlin Huan Mingyue Zhao Xixuan Liu High-tech Institute Qingzhou Shandong China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This paper proposes an anomaly detection method for course assessment results combined with the variational autoencoder (VAE) algorithm. This method identifies potential abnormal data points by analyzing the distribution characteristics of students' scores in course assessments, so as to timely detect abnormal situations of student grade fluctuations, such as cheating and grading errors. First, this paper preprocesses the data, including removing missing values and standardizing. Then, the variational autoencoder is used for feature extraction. The VAE model uses the encoder to compress high dimensional data to low dimension latent space, and then reconstructs the raw data by decoding. The paper can detect the data points which deviate significantly from the model. The experimental results show that the proposed algorithm can effectively identify abnormal data, and has higher accuracy and lower false positive rate than traditional methods. The variational autoencoder algorithm has good stability and accuracy when processing large-scale data. The evaluation indicators such as precision, recall rate and F1 value in the experiment all show the superiority of this method in anomaly detection, especially when dealing with large fluctuations in student grades, it shows a strong ability to identify anomalies.

关键词： Training Fluctuations Accuracy Autoencoders Feature extraction inference algorithms Stability analysis Robustness Anomaly detection Monitoring

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A Bandwidth-Scalable CNN-based Channel Estimation Algorithm for 5G Systems

A Bandwidth-Scalable CNN-based Channel Estimation Algorithm ...

引用

IEEE Radio and Wireless Symposium (RWS formerly RAWCON)

作者： Fábio D. L. Coutinho Hugerles S. Silva Arnaldo S. R. Oliveira Instituto de Telecomunicações DETI Universidade de Aveiro Aveiro Portugal Instituto de Telecomunicações DEE Universidade de Brasilia Brasília Brazil

ISBN: (数字)9798350377743

ISBN: (纸本)9798350377750

This work proposes a bandwidth-scalable channel estimation algorithm based on convolutional neural networks (CNNs) in line with fifth-generation (5G) standards. Support for different bandwidth configurations is essential to meet the heterogeneous and challenging demands of the 5G and next-generation wireless communication systems. Typically, each bandwidth configuration requires a dedicated CNN architecture, which also implies an individual training process. Our approach introduces a single module adaptable to all bandwidth parameters. Promising results for a subcarrier spacing of 15 kHz are achieved considering the mean-square error (MSE) and bit error rate (BER) performance metrics. Furthermore, an online inference on a field-programmable gate array (FPGA) is conducted to evaluate a comprehensive trade-off analysis between performance, computational efficiency, scalability, and generalizability.

关键词： 5G mobile communication Heuristic algorithms Scalability Channel estimation Bandwidth Computer architecture inference algorithms Computational efficiency Convolutional neural networks Field programmable gate arrays

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Late Breaking Results: Dynamically Scalable Pruning for Transformer-Based Large Language Models

Late Breaking Results: Dynamically Scalable Pruning for Tran...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Junyoung Lee Shinhyoung Jang Seohyun Kim Jongho Park Il Hong Suh Hoon Sung Chwa Yeseong Kim DGIST Coga Robotics

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

We propose Matryoshka, a novel framework for transformer model pruning, enabling dynamic runtime controls while maintaining competitive accuracy to modern large language models (LLMs). Matryoshka incrementally constructs submodels with varying complexities, allowing runtime adaptation without maintaining separate models. Our evaluations on LLaMA-7B demonstrate that Matryoshka achieves up to 34% speedup and outperforms the quality of state-of-the-art pruning methods, providing a flexible solution for deploying LLMs.

关键词： Adaptation models Runtime Accuracy Large language models Heuristic algorithms Computational modeling Europe Transformers Real-time systems inference algorithms

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LightMamba: Efficient Mamba Acceleration on FPGA with Quantization and Hardware Co-design

LightMamba: Efficient Mamba Acceleration on FPGA with Quanti...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Renjie Wei Songqiang Xu Linfeng Zhong Zebin Yang Qingyu Guo Yuan Wang Runsheng Wang Meng Li Institute for Artificial Intelligence School of Integrated Circuits Peking University Beijing China School of Software and Microelectronics Peking University Beijing China School of Electronic and Computer Engineering Peking University Shenzhen China Beijing Advanced Innovation Center for Integrated Circuits Beijing China Institute of Electronic Design Automation Peking University Wuxi China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

State space models (SSMs) like Mamba have recently attracted much attention. Compared to Transformer-based large language models (LLMs), Mamba achieves linear computation complexity with the sequence length and demonstrates superior performance. However, Mamba is hard to accelerate due to the scattered activation outliers and the complex computation dependency, rendering existing LLM accelerators inefficient. In this paper, we propose LightMamba that co-designs the quantization algorithm and FPGA accelerator architecture for efficient Mamba inference. We first propose an FPGA-friendly post-training quantization algorithm that features rotation-assisted quantization and power-of-two SSM quantization to reduce the majority of computation to 4-bit. We further design an FPGA accelerator that partially unrolls the Mamba computation to balance the efficiency and hardware costs. Through computation reordering as well as fine-grained tiling and fusion, the hardware utilization and memory efficiency of the accelerator get drastically improved. We implement LightMamba on Xilinx Versal VCK190 FPGA and achieve 4.65~6.06 x higher energy efficiency over the GPU baseline. When evaluated on Alveo U280 FPGA, LightMamba reaches 93 tokens/s, which is 1.43 x that of the GPU baseline.

关键词： Quantization (signal Memory management Graphics processing units Transformers Rendering (computer graphics Hardware inference algorithms Energy efficiency Computational efficiency Field programmable gate arrays

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LGNet: Linear Graph Representation for Efficient Cold-Start Recommendations

LGNet: Linear Graph Representation for Efficient Cold-Start ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhenglong Li Ruiqi Luo Bangchao Wang Lin Li Xian Zhong School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Convolutional Networks (GCNs) demonstrate significant potential in recommendation systems but face difficulties with the cold-start problem, especially in integrating new nodes during inference. The typical solution leverages meta-learning for few-shot learning, though it often fails to fully capture collaborative filtering between nodes. In this paper, we revisit the node embedding propagation algorithm in GCNs, emphasizing the importance of collaborative filtering and elucidating the relation between high-order and low-order embeddings. Given the substantial interaction data required for training recommendation models, maintaining a simple model structure remains crucial. To address these challenges, we propose the Linear Graph Network (LGNet), which theoretically compresses multi-layer GCNs into a single layer, enabling the embedding of new nodes during inference. Experimental results on benchmark datasets for link prediction and user cold-start tasks demonstrate that LGNet outperforms existing methods. The code will be available at https://***/kunbeibei/LGNet.

关键词： Training Metalearning Collaborative filtering Signal processing algorithms Predictive models Prediction algorithms Data models inference algorithms Speech processing Recommender systems

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