Low-power, low-mismatch, highly-dense array of VLSI Mihalas-Niebur neurons
| Autor: | Ralph Etienne-Cummings, Christian Brandli, Jamal Lottier Molin, Vigil Varghese, Adebayo Eisape, Chetan Singh Thakur |
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| Rok vydání: | 2017 |
| Předmět: |
Very-large-scale integration
business.industry Computer science Electrical engineering 02 engineering and technology Power (physics) Synapse 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure CMOS 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Neuron business 030217 neurology & neurosurgery Energy (signal processing) |
| Zdroj: | ISCAS |
| DOI: | 10.1109/iscas.2017.8050933 |
| Popis: | We present an array of Mihalas-Niebur neurons with dynamically reconfigurable synapses implemented in 0.5 μm CMOS technology optimized for low-power, low-mismatch, and high-density. This neural array has two modes of operation: one is each cell in the array operates as independent leaky integrate-and-fire neurons, and the second is two cells work together to model the Mihalas-Niebur neuron dynamics. Depending on the mode of operation, this implementation consists of 2040 Mihalas-Niebur neurons or 4080 I&F neurons within a 3mm χ 3mm area. Each I&F neuron cell consumes an area of 1495μm2 and the neural array dissipates 360pJ of energy per synaptic event measured at 5.0V power supply (∼14pJ at 1.0V estimated from SPICE simulation). |
| Databáze: | OpenAIRE |
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