Bit Error Tolerance Metrics for Binarized Neural Networks

TL;DR

This paper investigates the internal mechanisms of bit error tolerance in binarized neural networks (BNNs) by proposing new metrics that quantify neuron-level and inter-neuron error resilience, aiding the development of more efficient NN systems.

Contribution

It introduces two novel metrics to analyze and understand the principles behind bit error tolerance in BNNs, addressing a gap in current research.

Findings

Metrics are strongly related to bit error tolerance.

Proposed metrics effectively quantify neuron and inter-neuron error resilience.

Insights can guide the design of more robust neural networks.

Abstract

To reduce the resource demand of neural network (NN) inference systems, it has been proposed to use approximate memory, in which the supply voltage and the timing parameters are tuned trading accuracy with energy consumption and performance. Tuning these parameters aggressively leads to bit errors, which can be tolerated by NNs when bit flips are injected during training. However, bit flip training, which is the state of the art for achieving bit error tolerance, does not scale well; it leads to massive overheads and cannot be applied for high bit error rates (BERs). Alternative methods to achieve bit error tolerance in NNs are needed, but the underlying principles behind the bit error tolerance of NNs have not been reported yet. With this lack of understanding, further progress in the research on NN bit error tolerance will be restrained. In this study, our objective is to investigate the internal changes in the NNs that bit flip training causes, with a focus on binarized NNs (BNNs). To this end, we quantify the properties of bit error tolerant BNNs with two metrics. First, we propose a neuron-level bit error tolerance metric, which calculates the margin between the pre-activation values and batch normalization thresholds. Secondly, to capture the effects of bit error tolerance on the interplay of neurons, we propose an inter-neuron bit error tolerance metric, which measures the importance of each neuron and computes the variance over all importance values. Our experimental results support that these two metrics are strongly related to bit error tolerance.