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检索条件"主题词=Inference algorithms"

共 5376 条记录，以下是151-160 订阅

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Spatial Non-Stationary Channel Estimation for XL-MIMO Systems via Alternating MAP

Spatial Non-Stationary Channel Estimation for XL-MIMO System...

引用

IEEE Conference on Wireless Communications and Networking

作者： Wenkang Xu An Liu Minjian Zhao College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We investigate a joint visibility region (VR) detection and channel estimation problem in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, where nearfield propagation and spatial non-stationary effects exist. In this case, each scatterer can only see a subset of antennas, i.e., it has a certain VR over the antennas. A novel alternating maximum a posteriori (MAP) framework is developed for high-accuracy VR detection and channel estimation, which consists of three basic modules: a channel estimation module, a VR detection module, and a grid update module. Specifically, the first module is a low-complexity inverse-free variational Bayesian inference (IF-VBI) algorithm that avoids the matrix inverse via minimizing a relaxed Kullback-Leibler (KL) divergence. The second module is an expectation propagation (EP) algorithm that can recover binary VRs. And the third module refines polar-domain grid parameters via gradient ascent. Simulations demonstrate the superiority of the proposed algorithm in both VR detection and channel estimation.

关键词： Error analysis Heuristic algorithms Channel estimation inference algorithms Vectors MIMO Bayes methods Antennas Signal to noise ratio

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Learned ReLU-Based Soft Thresholding: A Data-Driven Method for Non-Negative Sparse Signal Recovery

Learned ReLU-Based Soft Thresholding: A Data-Driven Method f...

引用

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

作者： Akash Sen Pradyumna Pradhan Ramunaidu Randhi C. S. Sastry Department of Mathematics Indian Institute of Technology Hyderabad India Department of Humanities and Sciences Indian Institute of Petroleum and Energy Visakhapatnam India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Linear Inverse Problems (LIPs) with non-negative sparse constraints on the target signal are critical in numerous applications across various fields. Among the recently proposed methods dealing with LIPs, model-based deep learning methods, particularly deep unrolling, have gained popularity due to their interpretability, efficiency, and superior performance compared to traditional iterative methods. In this work, we propose a model-driven deep learning method by unrolling the recently developed ReLU-based Hard Thresholding (RHT) algorithm for non-negative sparse signal recovery. Specifically, we employ soft thresholding activation instead of hard thresholding in the unrolling of RHT, which enhances the model performance during backpropagation. The primary advantages of the proposed Learned ReLU-based Soft Thresholding (LRST) include interpretability and faster inference after the network is trained. Our numerical experiments demonstrate that the proposed LRST outperforms its classical counterparts, showcasing its potential for more effective non-negative sparse signal recovery.

关键词： Deep learning Inverse problems Lips Signal processing algorithms Signal processing Network architecture inference algorithms Numerical models Iterative methods Speech processing

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EE4: The Next Decade of AI – Barriers, Opportunities, & Directions

EE4: The Next Decade of AI – Barriers, Opportunities, & Dir...

引用

IEEE International Conference on Solid-State Circuits (ISSCC)

EE4: The Next Decade of AI – Barriers, Opportunities, & Directions.

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

EE4: The Next Decade of AI – Barriers, Opportunities, & Directions.

关键词： Artificial intelligence Computational modeling Technological innovation Silicon Data centers Solid state circuits Smart phones Sensors Regulation inference algorithms

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Late Breaking Results: AFS: Improving Accuracy of Quantized Mamba via Aggressive Forgetting Strategy

Late Breaking Results: AFS: Improving Accuracy of Quantized ...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Zhouquan Liu Libo Huang Ling Yang Gang Chen Wei Liu Mingche Lai Yongwen Wang College of Computer Science and Technology National University of Defense Technology Changsha China Key Laboratory of Advanced Microprocessor Chips and Systems Changsha China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Mamba overcomes the quadratic complexity problem inherent in Transformer models while maintaining comparable contextual modeling capabilities. However, Mamba-based foundation models encounter challenges in achieving efficient inference on resource-constrained devices, primarily due to their considerable size. Model compression techniques, such as linear quantization, offer a viable solution to this problem. Nevertheless, the introduction of significant outliers during Mamba's past-state forgetting process can lead to a notable decrease in accuracy when employing linear quantization. To overcome these challenges, this paper introduces the Aggressive Forgetting Strategy (AFS), an innovative and efficient algorithm designed to mitigate the quantization issues caused by outliers in the state forgetting mechanism. AFS incorporates a computation-free approach for handling outliers, facilitating both efficient and accurate linear quantization for Mamba, which is essential for applications in resource-constrained scenarios. By leveraging the AFS strategy, Mamba can perform more efficient inference, while significantly improving the accuracy by up to 21.5× compared to conventional methods.

关键词： Performance evaluation Quantization (signal) Accuracy Foundation models Logic gates Transformers inference algorithms Complexity theory Standards Context modeling

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Algorithm-Hardware Co-Design of a Unified Accelerator for Non-Linear Functions in Transformers

Algorithm-Hardware Co-Design of a Unified Accelerator for No...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Haonan Du Chenyi Wen Zhengrui Chen Li Zhang Qi Sun Zheyu Yan Cheng Zhuo Zhejiang University Hubei University Of Technology

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Nonlinear functions (NFs) in Transformers require high-precision computation consuming significant time and energy, despite the aggressive quantization schemes for other components. Piece-wise Linear (PWL) approximation-based methods offer more efficient processing schemes for NFs but fall short in dealing with functions with high nonlinearities. Moreover, PWL-based methods still suffer from inevitably high latency introduced by the Multiply-And-Add (MADD) unit. To address these issues, this paper proposes a novel quadratic approximation scheme and a highly integrated, multiplier-less hardware structure, as a unified method to accelerate any unary nonlinear function. We also demonstrate implementation examples for GELU, Softmax, and LayerNorm. The experimental results show that the proposed method achieves up to 5.41% higher inference accuracy and 60.12% lower area-delay product.

关键词： Accuracy Quantization (signal) Computational modeling Europe Life estimation Transformers Approximation algorithms Hardware inference algorithms Integrated circuit modeling

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

Late Breaking Results: Hyperdimensional Regression with Fine...

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

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

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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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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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SEDG: Stitch-Compatible End-to-End Layout Decomposition Based on Graph Neural Network

SEDG: Stitch-Compatible End-to-End Layout Decomposition Base...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Yifan Guo Jiawei Chen Yexin Li Yunxiang Zhang Qing Zhang Yuhang Zhang Yongfu Li Department of Micro-Nano Electronics Shanghai Jiao Tong University Shanghai China School of Integrated Circuit Science and Engineering East China Normal University Shanghai China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Advanced semiconductor lithography faces significant challenges as feature sizes continue to shrink, necessitating effective Multiple Patterning Layout Decomposition (MPLD) algorithms. Existing MPLD algorithms are inefficient or cannot support stitch insertion to achieve finer-grained optimal decom-position. This paper introduces an end-to-end GNN-based frame-work that not only achieves high-quality solutions quickly but also applies to layouts with stitches. Our framework treats layouts as heterogeneous graphs and performs inference through a message-passing mechanism. We deliver ultra-competitive, near-optimal solutions that are 10x faster than the exact algorithm (e.g., integer linear programming) and 3x faster than approximate algorithms (e.g., exact-cover, semi-definite programming).

关键词： Runtime Message passing Layout Lithography Europe Integer linear programming Approximation algorithms inference algorithms Graph neural networks Faces

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Insulators Defect Detection for Power Grid Based on Improved Yolov7 Algorithm

Insulators Defect Detection for Power Grid Based on Improved...

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Zhiwei Zhang Yan Zhuang Zhida Liang Shuai Kong Shengyi Zhu School of Control Science and Engineering Dalian University of Technology Dalian China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province Zhejiang Academy of Special Equipment Science Hangzhou China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

High-current out-of-phase closed bus is an essential link in the transmission of electric energy and is widely used in thermal power stations, hydropower stations and nuclear power plants. Although these busbars have the advantages of excellent dustproof effect and strong magnetic shielding, they are susceptible to insulator aging and damage in long-term operation, posing a potential safety risk to the power grid. Due to the enclosed space inside the bus and the complexity of external maintenance tasks, manual inspection has problems such as inefficiency and low safety. In order to solve these problems, this paper proposes an improved detection algorithm based on Yolov7 for insulator defect detection in hydropower station scenarios. Because of the particularity of Yolov7 network structure and application scenarios, this paper proposes a Channel Feature Enhancement module to enhance the channel representation ability of the network. In addition, an Edge Feature Enhancement module is introduced to improve the boundary detection of insulators by enhancing the contrast between the target and the background. Considering the limitations of existing studies in specific scenarios and the scarcity of dataset, this paper constructs and accurately annotates an insulator defect detection dataset applied to hydropower station scenarios. Experimental results show that the improved-Yolov7-tiny algorithm in this paper achieves a balance between accuracy and inference speed.

关键词： Accuracy Image edge detection Space power stations Hydroelectric power generation Inspection Insulators Feature extraction Power grids inference algorithms Defect detection

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