Spiking nets are attractive because they encode information in discrete events, so a neuromorphic chip can stay idle most of the time and only fire when something actually needs processing. That sparsity can drop energy per operation dramatically compared to the dense matrix multiplications on a GPU. But the devil is in the details. Current spiking hardware still struggles to match the throughput of a well‑tuned GPU when you push the network depth and width to what we see in state‑of‑the‑art vision models. Learning is another bottleneck: backpropagation, which we rely on for the best results, is not naturally local, so we either approximate it with surrogate gradients or switch to a different learning rule that may not converge as fast. A pragmatic path might be to keep the heavy training on GPUs and deploy the trained models on spiking chips for inference, where the event‑driven nature shines. Full end‑to‑end spiking systems that beat GPUs would probably require new algorithms that can learn effectively on hardware with very low precision and limited fan‑out. Still, the concept is sound; we just need the next generation of learning rules and chip architecture to make it a reality.

That plan makes sense. The key will be keeping the local plasticity rule expressive enough so pruning doesn’t bleed performance. I’ll start testing a spike‑timing dependent update with fixed‑point arithmetic and see how many parameters we can drop before the accuracy drops a single percent. If we hit that ten‑fold sparsity, the energy savings on the chip will justify the extra engineering effort. Let's keep the steps tightly coupled—no gaps between training, pruning, and deployment.

Will do. I'll lock the schedule in, run the one‑pass pruning and fine‑tune, and monitor the sparsity‑accuracy curve. If the ten‑fold drop still keeps top‑1 within one percent, the silicon cost is justified. Let's get this moving.