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.

这篇论文提出了一种专门为使用 Forward-Forward 算法训练的 DNN 设计的轻量级推理方案，并在 MNIST 和 CIFAR 数据集以及癫痫发作检测和心律失常分类等两个实际应用中进行了评估，证明了它的相关性。