In a world where digital technology reigns supreme, an unexpected contender is emerging: analog computing. Once considered obsolete, analog’s unique ability to handle complex, real-world data in a fluid and continuous way is turning heads, especially in artificial intelligence (AI). With the growing demands of AI—where speed, energy efficiency, and complex calculations are paramount—analog computing may hold the key to groundbreaking advances in fields from deep learning to autonomous technology.
Analog computers operate differently from the digital chips we’re familiar with. Rather than processing data in step-by-step sequences, analog systems use physical components—think of them as intricate arrangements of “pipes” and “valves” for electricity—that naturally perform mathematical operations. These include tools such as operational amplifiers, resistors, capacitors, and in some advanced designs, memristors. In deep learning, matrix manipulation (a key step where data is transformed layer by layer through a network of “neurons”) can be cumbersome on digital systems. Analog computers, however, can execute these complex multiplications fluidly, thanks to their ability to directly map voltages across resistors to represent specific weights in a neural network. When voltages, acting as input signals for one vector in the matrix, are applied across these resistors, they generate proportional currents that flow into specific nodes in the circuit. These resulting currents, collected at each node, effectively create outputs that represent the calculated product of the matrices, mimicking the dot product operations essential to AI algorithms.
The unique efficiency of analog computing lies in its ability to process these operations continuously and in parallel, meaning that each circuit node performs a calculation instantaneously as the input voltages are applied. Unlike digital processors, which handle tasks sequentially and require data storage and retrieval steps, analog computers directly encode and process data within physical hardware. This structural advantage allows for significant reductions in time and energy consumption, as calculations can occur simultaneously rather than in a step-by-step manner. Additionally, advanced analog components like memristors, which store specific resistance values, provide these systems with a memory-like function that is valuable in machine-learning applications. Memristors can retain weight values even after the power is turned off, making them suitable for processes such as backpropagation in neural networks, where weight adjustments based on previous training cycles are essential for accurate learning. Furthermore, memristors and other evolving analog components introduce the possibility of non-volatile memory in analog computers, enabling them to “remember” and refine the learned weights and biases in a network over time. This quality is especially beneficial in deep learning applications that require iterative adjustments to the model, allowing for greater learning accuracy and less energy usage compared to volatile memory systems in traditional digital hardware.
The real promise of analog computing for AI lies in its potential to reduce the energy and time demands that are problematic in digital systems. Digital computers, for instance, need to shuffle data back and forth between memory and processing units—a costly, time-consuming process. Analog systems bypass this entirely by directly encoding data as physical states (like resistance levels), resulting in substantial energy savings. This feature could be a game-changer for devices like autonomous drones, which need both efficiency and processing power in real time. Although analog faces challenges—such as susceptibility to noise and sensitivity to the environment—hybrid models that combine analog’s power-saving parallelism with digital precision could provide an ideal solution.
By merging analog’s continuous, “real-world” processing with digital’s accuracy, AI applications could see dramatic improvements. Analog computing might soon unlock faster, more accessible, and sustainable AI, ushering in advancements across diverse fields from smart devices to real-time analytics in critical industries. With its ability to operate smoothly and efficiently, analog computing just might be the innovative leap that AI has been waiting for.
Sources:
Gregersen, Erik. “Analog Computer | Definition, Examples, and Facts.” Encyclopedia Britannica, 3 Oct. 2024, www.britannica.com/technology/analog-computer.
Levy, Max G. “What Is Analog Computing? | Quanta Magazine.” Quanta Magazine, 2 Aug. 2024, www.quantamagazine.org/what-is-analog-computing-20240802.
Platt, Charles. “The unbelievable zombie comeback of analog computing.” WIRED, 30 Mar. 2023, www.wired.com/story/unbelievable-zombie-comeback-analog-computing.
