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Neuromorphic Processors Lag Behind GPUs for Large AI Models Despite Energy Advantages

Neuromorphic chips such as Intel Loihi promise major energy savings by activating only when events occur, yet data centers remain dominated by GPUs optimized for dense matrix operations in generative AI. The mismatch arises because modern large language models rely on high-precision linear algebra, massive memory bandwidth, and mature software stacks that neuromorphic architectures currently lack. Researchers are exploring spiking transformers and on-chip plasticity via mechanisms like STDP, but scaling to billions of parameters remains challenging. Companies including BrainChip, SynSense, and SpiNNaker2 have released commercial neuromorphic hardware, yet the absence of standardized tools and clear performance gains for mainstream workloads slows adoption. Hybrid approaches combining neuromorphic blocks with in-memory computing, analog circuits, and photonic accelerators may define future heterogeneous systems rather than outright replacement of GPUs.