Boolean Logic as an Error feedback mechanism

Training machine learning models can often be a very challenging process, requiring significant computational resources and time. The use of DNNs on computing hardware such as mobile and IoT devices is becoming increasingly important. IoT devices often have limitations in terms of memory and computational capacity. Quantization is a potential solution to this problem (Courbariaux et al., 2015; Chmiel et al., 2021; Leconte et al., 2023). And in particular, Binary Neural Networks (BNNs) is a remarkably promising direction because it reduces both memory and inference latency simultaneously (Nguyen, 2023). Formaly, BNN training can be formulated as minimising the training loss with binary weights, i.e., min f(w); f(w) = E