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Large-scale artificial neural networks have shown significant promise inaddressing a wide range of classification and recognition applications.However, their large computational requirements stretch the capabilities ofcomputing platforms. The fundamental components of these neural networks arethe neurons and its synapses. The core of a digital hardware neuron consists ofmultiplier, accumulator and activation function. Multipliers consume most ofthe processing energy in the digital neurons, and thereby in the hardwareimplementations of artificial neural networks. We propose an approximatemultiplier that utilizes the notion of computation sharing and exploits errorresilience of neural network applications to achieve improved energyconsumption. We also propose Multiplier-less Artificial Neuron (MAN) for evenlarger improvement in energy consumption and adapt the training process toensure minimal degradation in accuracy. We evaluated the proposed design on 5recognition applications. The results show, 35% and 60% reduction in energyconsumption, for neuron sizes of 8 bits and 12 bits, respectively, with amaximum of ~2.83% loss in network accuracy, compared to a conventional neuronimplementation. We also achieve 37% and 62% reduction in area for a neuron sizeof 8 bits and 12 bits, respectively, under iso-speed conditions.

Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing