用斐波那契编码压缩量化神经无线电接收机，大幅降低功耗和内存占用。
Fibbinary-Based Compression and Quantization for Efficient Neural Radio Receivers
- 基于斐波那契编码设计非均匀量化与增量式量化策略。
- 乘法器功耗和面积分别降低45%和44%，内存减少63.4%。
- 适合资源受限设备部署，尤其适用于低功耗无线通信系统。
神经接收机相比传统接收机表现优异，但网络复杂度高导致计算开销大，在硬件受限设备上部署困难。本文提出量化与压缩双重优化策略：引入均匀与非均匀量化，如斐波那契码字量化（FCQ）；提出细粒度增量网络量化（INQ）以补偿量化损失；并设计两种新型无损压缩算法，有效压缩具有高度冗余的斐波那契量化参数序列。量化使乘法器功耗和面积分别降低45%和44%，与压缩结合可实现63.4%的内存缩减，同时性能仍优于传统接收机。
原文摘要 · Abstract (English)
Neural receivers have shown outstanding performance compared to the conventional ones but this comes with a high network complexity leading to a heavy computational cost. This poses significant challenges in their deployment on hardware-constrained devices. To address the issue, this paper explores two optimization strategies: quantization and compression. We introduce both uniform and non-uniform quantization such as the Fibonacci Code word Quantization (FCQ). A novel fine-grained approach to the Incremental Network Quantization (INQ) strategy is then proposed to compensate for the losses introduced by the above mentioned quantization techniques. Additionally, we introduce two novel lossless compression algorithms that effectively reduce the memory size by compressing sequences of Fibonacci quantized parameters characterized by a huge redundancy. The quantization technique provides a saving of 45\% and 44\% in the multiplier's power and area, respectively, and its combination with the compression determines a 63.4\% reduction in memory footprint, while still providing higher performances than a conventional receiver.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。