What computer architecture can learn from computational intelligence-and vice versa

We consider whether the seemingly disparate fields of computational intelligence (CI) and computer architecture can profit from each others' principles, results and experience. In the process, we identify important common issues, such as parallelism, distribution of data and control, granularity and regularity. We present two novel computer architectures which have profited from principles found in CI, and identify two constraints on CI to eliminate the hidden influence of the von Neumann model of computation.

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