Is AI Technology Hardware or Software-Based? Unveiling the Reality

As technologists evolve, they often start to view hardware and software not as separate entities, but as different implementation choices for achieving the same goal. One of the companies I'm collaborating with is pushing the boundaries of AI with commodity phones, even in 2021. They are leveraging high-end smartphones to replace tasks traditionally performed by custom hardware costing upwards of ￡70,000 to ￡80,000, showcasing the potential of AI beyond the realm of specialized hardware.

What is AI and How Does It Fit?

At its core, AI is about creating automation systems to solve specific problems that are reserved for humans. An algorithm, a procedure or formula for solving a problem, consists of a sequence of specific actions. Therefore, the technology is not inherently rooted in either hardware or software but in the abstract algorithms that underpin it. While one can manually perform the procedures using paper and pencil, the primary motivation for using hardware or software is to automate and offload human tasks, making processes more efficient and less error-prone.

The Role of Hardware and Software in AI

Hardware and software are implementations dedicated to the corresponding algorithms, which are described in an abstract format. This format is decoupled from the physical operation mechanics. The feasibility and implementability of the specified actions are crucial for the successful execution of the algorithm. As Dr. David Vandevoorde elucidates, AI is a category of machine learning algorithms based on artificial neural networks. These algorithms are abstract, mathematically based procedures that describe step-by-step how to perform a particular function or operation.

Implementing Algorithms in Software and Hardware

Deep learning algorithms can be implemented in various forms—software (e.g., Python, C, TensorFlow, Keras, and PyTorch) or in hardware as electronic circuits or systems. Today, many deep learning hardware accelerators are VLSI (Very Large Scale Integration) systems, which are predominantly digital. However, some designers have managed to incorporate analog and mixed-signal circuits into VLSI systems to implement deep learning algorithms. Additionally, researchers are exploring alternative computing paradigms, such as quantum computers, photonic ICs, and optoelectronic systems, for deep learning.

Manual Implementation and Reality

While it is possible to implement algorithms manually (e.g., sorting algorithms on pen and paper or moving cards on a table), such methods are inefficient and impractical. Implementing complex algorithms by hand would take an extremely long time. Thus, while manual implementation is theoretically possible, it is not the optimal or practical approach. The efficiency and speed of hardware and software implementations are critical in real-world applications.

Conclusion

While AI technology can be seen through the lens of both hardware and software, the focus is often on the algorithms that drive the automation. The choice between hardware and software depends on the specific needs of the application, efficiency, and the complexity of the tasks at hand. Understanding the interplay between these components is crucial for designing and implementing effective AI systems.