Efficient and robust bitstream processing in binarised neural networks

Abstract

In the neural network context, used in a variety of applications, binarised networks, which describe both weights and activations as single-bit binary values, provide computationally attractive solutions. A lightweight binarised neural network system can be constructed using only logic gates and counters together with a two-valued activation function unit. However, binarised neural networks represent the weights and the neuron outputs with only one bit, making them sensitive to bitflipping errors. Binarised weights and neurons are manipulated by the utilisation of bitstream processing with regard to stochastic computing to cope with this error sensitivity. Stochastic computing is shown to provide robustness for bit errors on data while being built on a hardware structure, whose implementation is simplified by a novel subtractionfree implementation of the neuron activation.

SC-IZ: A Low-Cost Biologically Plausible Izhikevich Neuron for Large-Scale Neuromorphic Systems Using Stochastic Computing

“…In contrast to previous approaches to hardware implementation of Izhikevich neurons, our proposal advocates for the utilization of stochastic computing (SC), an unconventional computing technique that has demonstrated effectiveness in specific applications [29][30][31][32][33][34][35][36]. It processes information using probabilities instead of conventional binary or real-valued representations [37].…”