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Zeroing In On Biological Computing


Artificial spiking neural networks need to replicate both excitatory and inhibitory biological neurons in order to emulate the neural activation patterns seen in biological brains. Doing this with CMOS-based designs is challenging because of the large circuit footprint required. However, researchers at HP Labs observed that one biologically plausible model, the Hodgkins-Huxley model, is math... » read more

Spiking Neural Networks Place Data In Time


Artificial neural networks have found a variety of commercial applications, from facial recognition to recommendation engines. Compute-in-memory accelerators seek to improve the computational efficiency of these networks by helping to overcome the von Neumann bottleneck. But the success of artificial neural networks also highlights their inadequacies. They replicate only a small subset of th... » read more