Neuromorphic Computing for Low-Power Artificial Intelligence
Classical computing is beginning to encounter fundamental limits of energy efficiency. This presents a challenge that can no longer be solved by strategies such as increasing circuit density or refining standard semiconductor processes. The growing computational and memory demands of artificial intelligence (AI) require disruptive innovation in how information is represented, stored, communicated, and processed. By leveraging novel device modalities and compute-in-memory (CIM), in addition to analog dynamics and sparse communication inspired by the brain, neuromorphic computing offers a promising path toward improvements in the energy efficiency and scalability of current AI systems. But ...
| Authors | Keshava Katti, Pratik Chaudhari, Deep Jariwala |
|---|---|
| Primary category | cs.AR |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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