Deep learning inference possible in embedded systems thanks to TrueNorth - IBM Blog Research

Scientists at IBM Research – Almaden have demonstrated that the TrueNorth brain-inspired computer chip, with its 1 million neurons and 256 million synapses, can efficiently implement inference with deep networks that approach state-of-the-art classification accuracy on several vision and speech datasets. The essence of the innovation was a new algorithm for training deep networks to run efficiently on a neuromorphic architecture, such as TrueNorth, by using 1-bit neural spikes, low-precision synapses, and constrained block-wise connectivity--a task that was previously thought to be difficult, if not, impossible. "The goal of brain-inspired computing is to deliver a scalable neural network substrate while approaching fundamental limits of time, space, and energy," said IBM Fellow Dharmendra Modha, chief scientist, Brain-inspired Computing, IBM Research. Today, the TrueNorth development ecosystem includes not only the TrueNorth brain-inspired processor, the novel algorithm for training deep networks and the scaled-up NS16e System but also a simulator, a programming language, an integrated programming environment, a library of algorithms and applications, firmware, a teaching curriculum, single-chip boards, and scaled-out systems.