Deep learning has opened new opportunities in speech processing and speech coding. Neural vocoders outperform conventional approaches in terms of perceptual quality of reconstructed speech. However, approaches based on deep learning still suffer from complexity issues, which make the deployment on edge devices challenging. In this thesis we present the analysis and the optimizations performed on Streamwise-StyleMelGAN (SSMGAN), a neural vocoder able to synthesize high-quality wideband speech at 1.6 kbps. First, we present the ML compiler Apache TVM and assess the performance of standard (non-optimized) SSMGAN on ARM CPUs, showing how the baseline model is significantly slower than real-time on edge devices.