Key Takeaways
- AI algorithmic processing is transforming software structures and modifying processing hardware, diminishing the reliance on traditional CPU architectures.
- There is a paradigm shift in software development where software is increasingly dominating hardware capabilities, especially with the rise of AI applications.
- AI applications necessitate specialized hardware accelerators (like GPUs) due to their complex computational demands compared to traditional software applications.
The Impact of AI on Software and Hardware Development
Part 4 of this series analyzes how AI algorithmic processing is transforming software structures and significantly modifying processing hardware. It explores the marginalization of the traditional CPU architecture and demonstrates how software is increasingly dominating hardware. Additionally, it examines the impact of these changes on software development methodologies.
From “software eating the world” to “software consuming the hardware”
Energized by the exponential adoption of a multitude of AI applications, the software development landscape is on the brink of a paradigm shift, potentially mirroring the software revolution prophesied by venture capitalist Marc Andreessen in his seminal 2011 Wall Street Journal piece, “Why Software Is Eating The World”[1] (see Introduction in Part 1.) Strengthening this perspective is Microsoft co-founder Bill Gates’ belief in Generative AI’s (GenAI) transformative potential. He positions GenAI as the next paradigm-shifter, alongside the microprocessor, the personal computer, the internet and the mobile phone.[2]
The evolving landscape has given rise to an updated mantra: “Software is eating the world, hardware is feeding it, and data is driving it.”[3] Now, software is consuming the hardware. The outcome stems from the increasingly intertwined relationship between software and hardware. Software advancements not only drive innovation, but also redefine the very fabric of hardware design and functionality. As software becomes more complex, it pushes the boundaries of hardware, demanding ever-more powerful and specialized tools to drive its growth.
Traditional Software Applications versus AI Applications and the Impact on Processing Hardware
It is noteworthy to compare traditional software applications vis-à-vis AI applications to understand the evolving software and hardware scenarios.
Traditional Software Applications and CPU Processing
Traditional software applications are rule-based, captured in a sequence of pre-programmed instructions to be executed sequentially according to the intention of the software coder.
A Central-Processing-Unit (CPU) architecture – the dominating computing architecture since Von Neumann proposed it in 1945 – executes a traditional software program sequentially, in a linear fashion, one line after another, that dictates the speed of execution. To accelerate the execution, modern multi-core, multi-threading CPUs break down the entire sequence in multiple blocks of fewer instructions and process those blocks on the multi-cores and on the multi-threads in parallel.
Over the years, significant investments have been made to improve compiler technology, optimizing the partition of tasks into multiple independent blocks and threads to enhance execution speed. Yet nowadays the acceleration factor is not adequate to meet the processing power demands necessary for AI applications.
Significantly, when changes to traditional software programs are required, programmers must manually modify the code by replacing, adding or deleting instructions.
AI Software Applications and Hardware Acceleration
Unlike traditional software that follows a rigid script, AI applications harness the power of machine learning algorithms. These algorithms mimic the human brain’s structure by utilizing vast, interconnected networks of artificial neurons. While our brains evolved in millions of years and boast a staggering 86 billion neurons intricately linked, in the last decade, Artificial Neural Networks (ANNs) have grown exponentially from few neurons to hundreds of billions of neurons (artificial nodes) and connections (synapses).
For example, some of the largest neural networks used in deep learning models for tasks like natural language processing or image recognition may have hundreds of layers and billions of parameters. The exact number can vary depending on the specific architecture and application.
The complexity of an AI algorithm lies not in lines of code, but rather in the sheer number of neurons and associated parameters within its ANN. Modern AI algorithms can encompass hundreds of billions, even trillions, of these parameters. These parameters are processed using multidimensional matrix mathematics, employing integer or floating-point precision ranging from 4 bits to 64 bits. Though the underlying math involves basic multiplications and additions, these operations are replicated millions of times, and the complete set of parameters must be processed simultaneously during each clock cycle.
These powerful networks possess the ability to learn from vast datasets. By analyzing data, they can identify patterns and relationships, forming the foundation for Predictive AI, adept at solving problems and making data-driven forecasts, and Generative AI, focused on creating entirely new content.
AI software algorithms are inherently probabilistic. In other words, their responses carry a degree of uncertainty. As AI systems encounter new data, they continuously learn and refine their outputs, enabling them to adapt to evolving situations and improve response accuracy over time.
The computational demand for processing the latest generations of AI algorithms, such as transformers and large language models (LLMs), is measured in petaFLOPS (one petaFLOPS = 10^15 = 1,000,000,000,000,000 operations per second). CPUs, regardless of their core and thread count, are insufficient for these needs. Instead, AI accelerators—specialized hardware designed to significantly boost AI application performance—are at the forefront of development.
AI accelerators come in various forms, including GPUs, FPGAs, and custom-designed ASICs. These accelerators offer significant performance improvements over CPUs, resulting in faster execution times and greater scalability for managing increasingly complex AI applications. While a CPU can handle around a dozen threads simultaneously, GPUs can run millions of threads concurrently, significantly enhancing the performance of AI mathematical operations on massive vectors.
To provide higher parallel capabilities, GPUs allocate more transistors for data processing rather than data caching and flow control, whereas CPUs assign a significant portion of transistors for optimizing single-threaded performance and complex instruction execution. To date, the latest Nvidia’s Blackwell GPU includes 208 billion transistors, whereas Intel’s latest “Meteor Lake” CPU architecture contains up to 100 billion transistors.
The Bottom Line
In summary, traditional software applications fit deterministic scenarios dominated by predictability and reliability. These applications benefit from decades of refinement, are well-defined, and are relatively easy to modify when changes are needed. The hardware technology processing these applications are CPUs that perform at adequate speeds and excel in re-programmability and flexibility. Examples of traditional software programs include word processors, image and video editing tools, basic calculators, and video games with pre-defined rules. The profile of a traditional software developer typically requires skills in software engineering, including knowledge and expertise in one or more programming languages and experience in software development practices.
In contrast, AI software applications perfectly fit evolving, data-driven scenarios that require adaptability and learning from past experiences. The hardware technology managing AI applications encompasses vast numbers of highly parallel processing cores that deliver massive throughputs at the expense of considerable energy consumption. Examples of AI applications include facial recognition software (which improves with more faces), recommendation engines (which suggest products based on past purchases), and self-driving cars (which adapt to changing road conditions). The job requirements to become an AI algorithm engineer include a diversity of skills. Beyond specific programming abilities and software development practices, thorough understanding and extensive experience in data science and machine learning are critical.
Table I summarizes the main differences between traditional software applications versus an AI software application.
Software Stack Comparison: Traditional Software versus AI Algorithms
Traditional software applications, once completed and debugged, are ready for deployment. Conversely, AI algorithms require a fundamentally different approach: a two-stage development process known as training and inference.
Training Stage
In the training or learning stage, the algorithm is exposed to vast amounts of data. By processing this data, the algorithm “learns” to identify patterns and relationships within the data. Training can be a computationally intensive process, often taking weeks or even months depending on the complexity of the algorithm and the amount of data. The more data processed during training, the more refined and accurate the algorithm becomes.
Inference Stage
Once trained, the AI model is deployed in the inference stage for real-world use. During this phase, the algorithm applies what it has learned to new, unseen data, making predictions or decisions in real-time. Unlike traditional software, AI algorithms may continue to evolve and improve even after deployment, often requiring ongoing updates and retraining with new data to maintain or enhance performance.
This two-stage development process is reflected in the software stack for AI applications. The training stage often utilizes specialized hardware like GPUs (Graphics Processing Units) to handle the massive computational demands. The inference stage, however, may prioritize efficiency and resource optimization, potentially running on different hardware depending on the application.
AI software applications are built upon the core functionalities of a traditional software stack but necessitate additional blocks specific to AI capabilities. The main differences comprise:
- Data Management Tools: For collecting, cleaning, and preprocessing the large datasets required for training.
- Training Frameworks: Platforms such as TensorFlow, PyTorch, or Keras, which provide the infrastructure for building and training AI models.
- Monitoring and Maintenance Tools: Tools to monitor the performance of the deployed models, gather feedback, and manage updates or retraining as necessary.
Overall, the development and deployment of AI algorithms demand a continuous cycle of training and inference.
Validation of AI Software Applications: A Work-in-Progress
In traditional software, validation focuses on ensuring that the application meets expected functional requirements and operates correctly under defined conditions. This can be achieved through rigorous validation procedures that aim to cover all possible scenarios. Techniques like unit testing, integration testing, system testing, and acceptance testing are standard practices.
In contrast, validating AI software requires addressing not only functional correctness but also assessing the reliability of probabilistic outputs. It involves a combination of traditional testing methods with specialized techniques tailored to machine learning models. This includes cross-validation, validation on separate test sets, sensitivity analysis to input variations, adversarial testing to assess robustness, and fairness testing to detect biases in predictions. Moreover, continuous monitoring and validation are crucial due to the dynamic nature of AI models and data drift over time.
Risk Assessment
Risks in traditional software validation typically relate to functional errors, security vulnerabilities, and performance bottlenecks. These risks are generally more predictable and easier to mitigate through systematic testing and validation processes.
Risks in AI software validation extend beyond functional correctness to include ethical considerations (e.g., bias and fairness), interpretability (understanding how and why the model makes decisions), and compliance with regulations (such as data privacy laws). Managing these risks requires a comprehensive approach that addresses both technical aspects and broader societal impacts.
AI development is rapidly evolving, as these algorithmic models become more sophisticated, verification will become more challenging.
Implications for AI systems development
The suppliers of AI training and inference solutions falls into two main categories. Companies such as NVIDIA develop their own, publicly available programming language (CUDA) and develop faster, more scalable and more energy efficient execution hardware for general purpose use. Hyperscale companies such as Meta develop more specialized AI accelerators (MTIA) that are optimized for their specific workloads. Both require that the software layers and the underlying hardware are optimized for maximum performance and lower energy consumption. These metrics need to be measured pre-silicon, as the AI architecture optimization – as opposed to the traditional Von Neumann architecture – is of central importance for success. Large companies such as NVIDIA and Meta, as well as startup companies such as Rebellions rely on hardware-assisted solutions with the highest performance to accomplish this optimization.
In Conclusion:
The widespread adoption of AI across a variety of industries, from facial/image recognition and natural language processing all the way to self-driving vehicles and generative AI elaboration, is transforming how we live and work. This revolution has ignited a dual wave of innovations. On the hardware side, it is thrusting a massive demand for faster and more efficient AI processing. On the software side, it is driving the creation of ever more complex, and sophisticated AI applications.
While traditional software excels at well-defined tasks with clearly defined rules, AI applications are ideally suited for situations that demand adaptation, learning from data, and handling complex, unstructured information. The evolving nature of AI software presents a new challenge in validation that existing methods are not fully equipped to address. A new wave of innovation in software validation is necessary, opening new opportunities to the software automation industry.
[1] https://www.wsj.com/articles/SB10001424053111903480904576512250915629460
[2] https://www.gatesnotes.com/The-Age-of-AI-Has-Begun.
[3] The quote “Software is eating the world, hardware is feeding it, and data is driving it” is attributed to Peter Levine, a venture capitalist at Andreessen Horowitz, who used it to encapsulate the transformative impact of software, hardware, and data on various industries and aspects of our lives.
Also Read:
The Immensity of Software Development the Challenges of Debugging (Part 1 of 4)
The Immensity of Software Development and the Challenges of Debugging Series (Part 2 of 4)
The Immensity of Software Development and the Challenges of Debugging (Part 3 of 4)
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