Artificial intelligence has a hardware problem.
The models are becoming more capable, but the infrastructure required to train and run them is also becoming more expensive, energy-intensive, and centralized. Traditional processors are extraordinarily good at deterministic computation, yet they were not designed to imitate the sparse, event-driven way biological brains process information.
That mismatch is why neuromorphic computing deserves more attention from developers.
What makes neuromorphic hardware different?
A conventional computer separates processing from memory. Data moves repeatedly between the processor and memory, creating latency and consuming energy. This architecture has served computing well for decades, but it becomes inefficient when a workload requires constant movement of large neural-network parameters.
