The rapid advancement of digital computing has led to significant challenges, particularly in the realm of energy consumption and processing limitations. As AI and machine learning applications become increasingly complex, the power requirements for training large neural networks have skyrocketed. For instance, training a single large language model can consume as much energy as 100 U.S. households use in an entire year1. This energy-intensive nature of digital computing not only raises environmental concerns but also poses scalability issues for future AI development.

Additionally, digital computers face inherent limitations in processing continuous data and performing parallel computations efficiently. The discrete nature of digital systems requires continuous signals to be quantized, leading to potential loss of information and increased computational overhead2. These challenges have prompted researchers to explore alternative computing paradigms, such as analog systems, which offer potential solutions for energy-efficient computing and more natural handling of continuous data streams, particularly in applications like edge computing and real-time signal processing34.

Analog computing offers several distinct advantages over digital systems, particularly in handling continuous data and performing parallel computations efficiently. Unlike digital computers, analog systems can process information in real-time without the need for quantization, making them ideal for applications involving natural phenomena and physical processes1. This inherent ability to work with continuous signals allows analog computers to solve complex differential equations and perform integral calculus with remarkable speed and accuracy2.

One of the most significant advantages of analog computing is its energy efficiency. Analog systems can perform certain calculations using significantly less power than their digital counterparts, making them particularly attractive for edge computing and IoT applications3. Additionally, analog computers excel at tasks requiring high levels of parallelism, such as neural network simulations, potentially offering a more natural and efficient approach to artificial intelligence processing24. As the demand for more powerful and energy-efficient computing solutions grows, the unique strengths of analog systems position them as a promising complement to digital technologies in addressing future computational challenges.

Analog computing is revolutionizing AI and edge applications by offering significant power efficiency gains and improved performance for specific tasks. Aspinity's analog ML chip, for instance, can perform always-on event detection while consuming only ~20 µA, enabling up to 105× power savings for applications like glass break detection compared to traditional digital schemes1. This ultra-low power consumption is crucial for battery-powered edge devices that require constant monitoring.

In edge AI scenarios, analog computing excels at handling continuous data streams from sensors without the need for power-hungry analog-to-digital converters. This approach provides greater data precision, particularly useful for time series data like audio processing2. Companies like Mythic are leveraging analog compute-in-memory architectures to deliver up to 25 TOPS (trillion operations per second) in a single chip for high-end edge AI applications, offering 3.8x less power consumption and 2.6x faster performance compared to industry-standard digital AI inference chips3. These advancements are enabling new possibilities in robotics, defense, security, and consumer devices, where power efficiency and real-time processing are critical.

Hybrid systems are poised to redefine the computing landscape by seamlessly integrating quantum and classical technologies. This approach leverages the strengths of both paradigms, where quantum computers handle complex calculations that classical systems struggle with, while classical computers excel at tasks like data preparation and error correction12. As quantum computing matures, hybrid systems are expected to become more prevalent, offering a practical solution to current computational challenges.

Key players in the tech industry are already advancing hybrid architectures. Companies like Microsoft and IBM are integrating quantum services into cloud platforms, enabling developers to write code that runs efficiently across both quantum and classical environments1. Intel's Horse Ridge chip exemplifies innovations in hybrid systems by simplifying the interface between control and measurement layers, crucial for maintaining functionality at extremely low temperatures1. These advancements highlight the potential of hybrid systems to enhance fault tolerance and reliability by using classical computers to detect and correct errors in quantum computations1. As these technologies evolve, hybrid systems are anticipated to play a critical role in fields such as artificial intelligence, cryptography, and complex simulations, paving the way for a new era of computing23.