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.

翻译：与传统接收器相比，神经接收器展现出卓越的性能，但这也伴随着较高的网络复杂度，导致沉重的计算成本。这对其在硬件受限设备上的部署构成了重大挑战。为解决这一问题，本文探讨了两种优化策略：量化和压缩。我们引入了均匀与非均匀量化方法，例如斐波那契码字量化（FCQ）。随后，提出了一种新颖的增量网络量化（INQ）策略的细粒度方法，以补偿上述量化技术引入的损失。此外，我们引入了两种新颖的无损压缩算法，通过压缩具有高度冗余性的斐波那契量化参数序列，有效减少了内存占用。该量化技术在乘法器的功耗和面积上分别实现了45%和44%的节省，与压缩技术结合后，内存占用减少了63.4%，同时仍能提供优于传统接收器的性能。