In a recent article, published in the Proceedings of the National Academy of Sciences, IBM researchers demonstrated that the TrueNorth chip, designed to perform neuromorphic computing, can be trained using deep learning algorithms.
The TrueNorth chip was designed to efficiently simulate the efficient modeling of spiking neural networks, a model for neurons that closely mimics the way biological neurons work. Spiking neural networks are based on the integrate and fire model, inspired on the fact that actual neurons integrate the incoming ion currents caused by synaptic firing and generate an output spike only when sufficient synaptic excitation has been accumulated. Spiking neural network models tend to be less efficient than more abstract models of neurons, which simply compute the real valued output directly from the values of the real valued inputs multiplied by the input weights.
As IEEE Spectrum explains: “Instead of firing every cycle, the neurons in spiking neural networks must gradually build up their potential before they fire. To achieve precision on deep-learning tasks, spiking neural networks typically have to go through multiple cycles to see how the results average out. That effectively slows down the overall computation on tasks such as image recognition or language processing.”
In the article just published, IBM researchers have adapted deep learning algorithms to run on their TrueNorth architecture, and have achieved comparable precision, with lower energy dissipation. This research raises the prospect that energy-efficient neuromorphic chips may be competitive in deep learning tasks.
Image from Wikimedia Commons