I propose a hybrid spiking neural network that combines spike counts and membrane potentials for output prediction, extended to both language modeling and image generation tasks. Key Findings (v4 NEW - Image Generation):- Spiking VAE with 50% membrane weight: 57% loss reduction vs spike-only- Posterior collapse solution: KL>0 achieved (spike-only had KL=0)- Image generation sparsity: 96% fewer spike operations- Optimal trade-off: 50% membrane weight balances quality and efficiency Key Findings (v3 - Language Model):- BitNet Mixed Precision: PPL 2.69 BEATS standard SNN (3.29)!- RWKV Time-Mixing: 36.1% improvement in long-range memory- Ultimate Architecture: 43.4% improvement combining all techniques- Multiplication-free reservoir: 50-70% of operations are additions only- 16-model ensemble achieves PPL 1.04 Key Findings (v1-v2):- SNN achieves BEST perplexity (PPL=9.90) vs DNN (11.28) and LSTM (15.67)- 14.7× more energy-efficient through sparse computation (only 7.6% of neurons fire)- 39.7% quality improvement from hybrid (spike + membrane) approach- Extreme compressibility: 80% neuron pruning and 4-bit quantization still work- Noise robust: No degradation at 30% input noise This v4 establishes hybrid SNNs as the optimal architecture for energy-efficient multimodal AI (language and vision) on edge devices. Source code: https://github.com/hafufu-stack/snn-language-model
Hiroto Funasaki (Wed,) studied this question.