  Much of the present-day Artificial Intelligence (AI) utilizes artificial
neural networks, which are sophisticated computational models designed to
recognize patterns and solve complex problems by learning from data. However, a
major bottleneck occurs during a device's calculation of weighted sums for
forward propagation and optimization procedure for backpropagation, especially
for deep neural networks, or networks with numerous layers. Exploration into
different methods of implementing neural networks is necessary for further
advancement of the area. While a great deal of research into AI hardware in
both directions, analog and digital implementation widely exists, much of the
existing survey works lacks discussion on the progress of analog deep learning.
To this end, we attempt to evaluate and specify the advantages and
disadvantages, along with the current progress with regards to deep learning,
for analog implementations. In this paper, our focus lies on the comprehensive
examination of eight distinct analog deep learning methodologies across
multiple key parameters. These parameters include attained accuracy levels,
application domains, algorithmic advancements, computational speed, and
considerations of energy efficiency and power consumption. We also identify the
neural network-based experiments implemented using these hardware devices and
discuss comparative performance achieved by the different analog deep learning
methods along with an analysis of their current limitations. Overall, we find
that Analog Deep Learning has great potential for future consumer-level
applications, but there is still a long road ahead in terms of scalability.
Most of the current implementations are more proof of concept and are not yet
practically deployable for large-scale models.
