Geometric Clustering for Hardware-Efficient Implementation of Chromatic Dispersion Compensation

• URL: http://arxiv.org/abs/2409.10416v1
• Date: Mon, 16 Sep 2024 15:48:05 GMT
• Title: Geometric Clustering for Hardware-Efficient Implementation of Chromatic Dispersion Compensation
• Authors: Geraldo Gomes, Pedro Freire, Jaroslaw E. Prilepsky, Sergei K. Turitsyn,
• Abstract summary: This paper provides a theoretical analysis of the tap overlapping effect in CDC filters for coherent receivers. We introduce a novel Time-Domain Clustered Equalizer (TDCE) technique based on this concept. We develop an innovative parallelization method for TDCE, implementing it in hardware for fiber lengths up to 640 km.
• Score: 2.8870882078316855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Power efficiency remains a significant challenge in modern optical fiber communication systems, driving efforts to reduce the computational complexity of digital signal processing, particularly in chromatic dispersion compensation (CDC) algorithms. While various strategies for complexity reduction have been proposed, many lack the necessary hardware implementation to validate their benefits. This paper provides a theoretical analysis of the tap overlapping effect in CDC filters for coherent receivers, introduces a novel Time-Domain Clustered Equalizer (TDCE) technique based on this concept, and presents a Field-Programmable Gate Array (FPGA) implementation for validation. We developed an innovative parallelization method for TDCE, implementing it in hardware for fiber lengths up to 640 km. A fair comparison with the state-of-the-art frequency domain equalizer (FDE) under identical conditions is also conducted. Our findings highlight that implementation strategies, including parallelization and memory management, are as crucial as computational complexity in determining hardware complexity and energy efficiency. The proposed TDCE hardware implementation achieves up to 70.7\% energy savings and 71.4\% multiplier usage savings compared to FDE, despite its higher computational complexity.

Related papers

• Accelerating Error Correction Code Transformers [56.75773430667148]
  We introduce a novel acceleration method for transformer-based decoders. We achieve a 90% compression ratio and reduce arithmetic operation energy consumption by at least 224 times on modern hardware.
  arXiv  Detail & Related papers  (2024-10-08T11:07:55Z)
• Dynamic Range Reduction via Branch-and-Bound [1.533133219129073]
  Key strategy to enhance hardware accelerators is the reduction of precision in arithmetic operations. This paper introduces a fully principled Branch-and-Bound algorithm for reducing precision needs in QUBO problems. Experiments validate our algorithm's effectiveness on an actual quantum annealer.
  arXiv  Detail & Related papers  (2024-09-17T03:07:56Z)
• ISO: Overlap of Computation and Communication within Seqenence For LLM Inference [8.616769297336708]
  This paper introduces a novel strategy for computation-communication overlap that operates at the sequence level. Experimental evaluations conducted using 30b/70b models have demonstrated significant improvements in efficiency.
  arXiv  Detail & Related papers  (2024-09-04T05:22:17Z)
• Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach [58.57026686186709]
  We introduce the Convolutional Transformer layer (ConvFormer) and propose a ConvFormer-based Super-Resolution network (CFSR) CFSR inherits the advantages of both convolution-based and transformer-based approaches. Experiments demonstrate that CFSR strikes an optimal balance between computational cost and performance.
  arXiv  Detail & Related papers  (2024-01-11T03:08:00Z)
• Check-Agnosia based Post-Processor for Message-Passing Decoding of Quantum LDPC Codes [3.4602940992970908]
  We introduce a new post-processing algorithm with a hardware-friendly orientation, providing error correction performance competitive to the state-of-the-art techniques. We show that latency values close to one microsecond can be obtained on the FPGA board, and provide evidence that much lower latency values can be obtained for ASIC implementations.
  arXiv  Detail & Related papers  (2023-10-23T14:51:22Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Fully Quantized Image Super-Resolution Networks [81.75002888152159]
  We propose a Fully Quantized image Super-Resolution framework (FQSR) to jointly optimize efficiency and accuracy. We apply our quantization scheme on multiple mainstream super-resolution architectures, including SRResNet, SRGAN and EDSR. Our FQSR using low bits quantization can achieve on par performance compared with the full-precision counterparts on five benchmark datasets.
  arXiv  Detail & Related papers  (2020-11-29T03:53:49Z)
• ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks [63.91384986073851]
  We propose the autoencoder-based low-rank filter-sharing technique technique (ALF) ALF shows a reduction of 70% in network parameters, 61% in operations and 41% in execution time, with minimal loss in accuracy.
  arXiv  Detail & Related papers  (2020-07-27T09:01:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.