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Nabil Imam, Filipp Akopyan, Paul Merolla, John Arthur, Rajit Manohar, and Dharmendra Modha
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 1024x256 synapses in 4.2mm2 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 fanout 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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