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A Digital Neurosynaptic Core Using Event-Driven QDI Circuits
We design and implement a key building block of a scalable neuromorphic architecture capable of running spiking neural networks in compact and low-power hardware. Our innovation is a configurable neurosynaptic core that combines 256 integrate-and-fire neurons, 1024 input axons, and 1024×256 synapses...
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Main Authors: | , , , , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Citations: | Items that cite this one |
Online Access: | Request full text |
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Summary: | We design and implement a key building block of a scalable neuromorphic architecture capable of running spiking neural networks in compact and low-power hardware. Our innovation is a configurable neurosynaptic core that combines 256 integrate-and-fire neurons, 1024 input axons, and 1024×256 synapses in 4.2mm 2 of silicon using a 45nm SOI process. We are able to achieve ultra-low energy consumption 1) at the circuit-level by using an asynchronous design where circuits only switch while performing neural updates, 2) at the core-level by implementing a 256 neural fan out in a single operation using a crossbar memory, and 3) at the architecture-level by restricting core-to-core communication to spike events, which occur relatively sparsely in time. Our implementation is purely digital, resulting in reliable and deterministic operation that achieves for the first time one-to-one correspondence with a software simulator. At 45pJ per spike, our core is readily scalable and provides a platform for implementing a wide array of real-time computations. As an example, we demonstrate a sound localization system using coincidence-detecting neurons. |
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ISSN: | 1522-8681 |
DOI: | 10.1109/ASYNC.2012.12 |