2021 IEEE Workshop on Signal Processing Systems (SiPS)最新文献_第3页

Low Resource Species Agnostic Bird Activity Detection 低资源物种不可知论鸟类活动检测

2021 IEEE Workshop on Signal Processing Systems (SiPS) Pub Date : 2021-10-01 DOI: 10.1109/SiPS52927.2021.00015

Mark Anderson, J. Kennedy, N. Harte

引用次数: 3

Digital Predistortion with Compressed Observations for Cloud-Based Learning 基于云学习的压缩观测数字预失真

2021 IEEE Workshop on Signal Processing Systems (SiPS) Pub Date : 2021-10-01 DOI: 10.1109/SiPS52927.2021.00016

Arne Fischer-Bühner, E. Matús, M. Gomony, L. Anttila, G. Fettweis, M. Valkama

{"title":"Digital Predistortion with Compressed Observations for Cloud-Based Learning","authors":"Arne Fischer-Bühner, E. Matús, M. Gomony, L. Anttila, G. Fettweis, M. Valkama","doi":"10.1109/SiPS52927.2021.00016","DOIUrl":"https://doi.org/10.1109/SiPS52927.2021.00016","url":null,"abstract":"This paper presents a novel system architecture for digital predistortion (DPD) of power amplifiers (PA), where the training of the DPD model is done in a remote compute infrastructure i.e. cloud or a distributed unit (DU). In beyond-5G systems it is no longer feasible to perform computationally intensive tasks such as DPD training locally in the radio unit front-end which has stringent power consumption requirements. Thus, we propose to split the DPD system and perform the compute-intensive DPD training in the DU where more processing resources are available. To enable the distant training, the observed PA output, i.e. the observation signal, must be available, however, sending the data-intensive observation signal to the DU adds additional communication overhead to the system. In this paper, a low-complexity compression method is proposed to reduce the bit-resolution of the observation signal by removing the known linear part in the observation to use fewer bits to represent the remaining information. Our numerical simulations show a reduction of 50 % of bits/samples for the accurate training of the DPD model. The DPD performance was evaluated based on simulation for a strongly driven PA operated at 28 GHz with a 200 MHz wide OFDM signal.","PeriodicalId":103894,"journal":{"name":"2021 IEEE Workshop on Signal Processing Systems (SiPS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126562690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time sliding window for the detection of CCSK frames 用于CCSK帧检测的时间滑动窗口

2021 IEEE Workshop on Signal Processing Systems (SiPS) Pub Date : 2021-10-01 DOI: 10.1109/SiPS52927.2021.00026

Camille Monière, Kassem Saied, Bertrand Le Gal, Emmanuel Boutillon

引用次数: 8

[Title page iii] [标题页iii]

2021 IEEE Workshop on Signal Processing Systems (SiPS) Pub Date : 2021-10-01 DOI: 10.1109/sips52927.2021.00002

引用次数: 0

Globally Assisted Instance Normalization for Bandwidth-Efficient Neural Style Transfer 带宽高效神经风格迁移的全局辅助实例归一化

2021 IEEE Workshop on Signal Processing Systems (SiPS) Pub Date : 2021-10-01 DOI: 10.1109/SiPS52927.2021.00019

Hsiu-Pin Hsu, Chao-Tsung Huang

引用次数: 0

Short Codes with Near-ML Universal Decoding: Are Random Codes Good Enough? 具有近ml通用解码的短代码:随机代码足够好吗?

2021 IEEE Workshop on Signal Processing Systems (SiPS) Pub Date : 2021-10-01 DOI: 10.1109/SiPS52927.2021.00025

Vivian Papadopoulou, Marzieh Hashemipour-Nazari, Alexios Balatsoukas-Stimming

{"title":"Short Codes with Near-ML Universal Decoding: Are Random Codes Good Enough?","authors":"Vivian Papadopoulou, Marzieh Hashemipour-Nazari, Alexios Balatsoukas-Stimming","doi":"10.1109/SiPS52927.2021.00025","DOIUrl":"https://doi.org/10.1109/SiPS52927.2021.00025","url":null,"abstract":"Short blocklength codes have an important role in machine-type and ultra-low-latency communications. Unfortunately, reducing the blocklength makes it very challenging to achieve good error-correcting performance. There exist near-ML decoding algorithms with manageable complexity for short blocklength codes, such as ordered statistics decoding and the more recent guessing random additive noise decoding algorithm. These algorithms have the additional advantage that they are universal, in the sense that they can decode any linear block code. For this reason, some recent works have attempted to construct unstructured linear codes for use with universal decoders using sophisticated techniques, such as reinforcement learning. In this work, we first describe a genetic-algorithm-aided (GA-aided) construction method for unstructured codes and we then compare a very simple random construction to both the GA-aided construction and the reinforcement learning construction. Our simulation results indicate that, while some care should be taken when selecting an unstructured code, sophisticated and complex code construction methods may not be necessary in the sense that they lead to minimal improvements.","PeriodicalId":103894,"journal":{"name":"2021 IEEE Workshop on Signal Processing Systems (SiPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131485988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Exploration and Generation of Efficient FPGA-based Deep Neural Network Accelerators 基于fpga的高效深度神经网络加速器的探索与生成

2021 IEEE Workshop on Signal Processing Systems (SiPS) Pub Date : 2021-10-01 DOI: 10.1109/SiPS52927.2021.00030

Nermine Ali, Jean-Marc Philippe, Benoît Tain, P. Coussy

{"title":"Exploration and Generation of Efficient FPGA-based Deep Neural Network Accelerators","authors":"Nermine Ali, Jean-Marc Philippe, Benoît Tain, P. Coussy","doi":"10.1109/SiPS52927.2021.00030","DOIUrl":"https://doi.org/10.1109/SiPS52927.2021.00030","url":null,"abstract":"Convolutional Neural Networks (CNNs) have emerged as an answer to next-generation applications such as complex image recognition and object detection. Embedding such compute-intensive and memory-hungry algorithms on edge systems will lead to smarter high-value applications. However, the algorithmic innovations in the CNN field leave the hardware accelerators one step behind. Reconfigurable hardware (e.g. FPGAs) allows designing custom accelerators adapted to new algorithms. Furthermore, new design approaches such as high-level synthesis (HLS) enable to generate RTL code based on high-level function descriptions. This paper presents a high-level CNN accelerator generation framework for FPGAs. A first phase of the framework characterizes CNN descriptions using hardware-aware metrics. These metrics then drive a hardware generation phase which builds the proper C source code implementation for each layer of the network. Finally, an HLS tool outputs the synthesizable RTL code of the accelerator. This approach aims at reducing the gap between the evolving applications based on artificial intelligence and hardware accelerators, thus reducing time-to-market of new systems.","PeriodicalId":103894,"journal":{"name":"2021 IEEE Workshop on Signal Processing Systems (SiPS)","volume":"520 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131886759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Implementation of a Two-Dimensional FFT/IFFT Processor for Real-Time High-Resolution Synthetic Aperture Radar Imaging 用于实时高分辨率合成孔径雷达成像的二维FFT/IFFT处理器的实现

2021 IEEE Workshop on Signal Processing Systems (SiPS) Pub Date : 2021-10-01 DOI: 10.1109/sips52927.2021.00045

Hung-Yuan Chin, P. Tsai, Sz-Yuan Lee

{"title":"Implementation of a Two-Dimensional FFT/IFFT Processor for Real-Time High-Resolution Synthetic Aperture Radar Imaging","authors":"Hung-Yuan Chin, P. Tsai, Sz-Yuan Lee","doi":"10.1109/sips52927.2021.00045","DOIUrl":"https://doi.org/10.1109/sips52927.2021.00045","url":null,"abstract":"The demand of high-resolution synthetic aperture radar (SAR) images entails large-size fast Fourier transform (FFT) in the range and azimuth directions and makes real-time processing a challenging task. A 2D-FFT/IFFT processor is implemented to support 8192-, 16384-, and 32768-point range FFT/IFFT and 8192-point azimuth FFT/IFFT. To exploit the burst read/write of external DDR memory for access efficiency, azimuth decomposition is adopted. Besides, normal-order input for azimuth FFT and bit-reversed order input for azimuth IFFT are designed to save latency and storage for re-ordering. The control logic for look-up tables of twiddle factors in normal-order FFT and bit-reversed-order IFFT given azimuth decomposition is derived and a significant ROM-table reduction is achieved. The radix-23 single-path delay feedback (SDF) architecture is employed to reduce the number of complex multipliers and to allow for streaming input/output. A customized floating-point data-path is utilized. The maximum operating frequency is 111MHz of our 2D-FFT/IFFT processor realized by Xilinx ultrascale VU37P HBM FPGA. The SQNR achieves more than 48dB for one transformation and about 38dB for successive 2D- FFT and 2D-IFFT operations. We demonstrate a promising solution of2D FFT/IFFT for real-time SARimaging.","PeriodicalId":103894,"journal":{"name":"2021 IEEE Workshop on Signal Processing Systems (SiPS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114738675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Scalable Generator for Massive MIMO Baseband Processing Systems with Beamspace Channel Estimation 基于波束空间信道估计的大规模MIMO基带处理系统的可扩展发生器

2021 IEEE Workshop on Signal Processing Systems (SiPS) Pub Date : 2021-10-01 DOI: 10.1109/SiPS52927.2021.00040

Yue Dai, Harrison Liew, M. Rasekh, Seyed Hadi Mirfarshbafan, Alexandra Gallyas-Sanhueza, James Dunn, Upamanyu Madhow, Christoph Studer, B. Nikolić

引用次数: 1

Exploration of Energy-Efficient Architecture for Graph-Based Point-Cloud Deep Learning 基于图的点云深度学习节能架构探索

2021 IEEE Workshop on Signal Processing Systems (SiPS) Pub Date : 2021-10-01 DOI: 10.1109/SiPS52927.2021.00054

Jie-Fang Zhang, Zhengya Zhang

引用次数: 1