  The human brain performs tasks with an outstanding energy-efficiency, i.e.,
with approximately 20 Watts. The state-of-the-art Artificial/Deep Neural
Networks (ANN/DNN), on the other hand, have recently been shown to consume
massive amounts of energy. The training of these ANNs/DNNs is done almost
exclusively based on the back-propagation algorithm, which is known to be
biologically implausible. This has led to a new generation of forward-only
techniques, including the Forward-Forward algorithm. In this paper, we propose
a lightweight inference scheme specifically designed for DNNs trained using the
Forward-Forward algorithm. We have evaluated our proposed lightweight inference
scheme in the case of the MNIST and CIFAR datasets, as well as two real-world
applications, namely, epileptic seizure detection and cardiac arrhythmia
classification using wearable technologies, where complexity overheads/energy
consumption is a major constraint, and demonstrate its relevance.
