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.

The amount of time, effort, and resources needed to train increasingly complicated neural network models is soaring as more machine learning experiments are being done. In order to combat this, a brand-new branch of artificial intelligence called "analog deep learning" is on the rise. It promises faster processing with far less energy consumption. Like transistors are the essential components of digital computers, programmable resistors are the fundamental building blocks of analog deep learning. Researchers have developed a network of analog artificial "neurons" and "synapses" that can do calculations similarly to a digital neural network by repeatedly repeating arrays of programmable resistors in intricate layers.

The newly developed material is compatible with silicon fabrication techniques and could pave the way for integration into commercial computing hardware for deep-learning applications. "With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages," said senior author Jesús A. del Alamo, the Donner Professor in MIT's Department of Electrical Engineering and Computer Science (EECS). "This work has really put these devices at a point where they now look really promising for future applications." The researchers further explained how they managed to increase the speed of the ions in the device. "The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime," explained senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.