Rathinakumar Appuswamy

TL;DR: Inspired by the brain’s structure, an efficient, scalable, and flexible non–von Neumann architecture is developed that leverages contemporary silicon technology and is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification.

TL;DR: This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.

TL;DR: This work introduces a novel means to estimate and scale the task loss gradient at each weight and activation layer's quantizer step size, such that it can be learned in conjunction with other network parameters.

TL;DR: This work treats spikes and discrete synapses as continuous probabilities, which allows training the network using standard backpropagation and naturally maps to neuromorphic hardware by sampling the probabilities to create one or more networks, which are merged using ensemble averaging.

TL;DR: A set of abstractions, algorithms, and applications that are natively efficient for TrueNorth, a non-von Neumann architecture inspired by the brain's function and efficiency, and seven applications that include speaker recognition, music composer recognition, digit recognition, sequence prediction, collision avoidance, optical flow, and eye detection are developed.