Comprehensive AI Development Software Critical To AI Democratization

Public cloud service providers and pure-play artificial intelligence (AI) software vendors are making huge progress in developing sophisticated AI applications, including more accurate computer vision, highly personalized recommendation systems, or more natural interactions with conversational AI. These players offer comprehensive features and tools to help AI developers and data scientists, which accelerates the development of their applications.

However, these efforts are not enough to simplify the AI development process. The AI development tools offered are often decoupled from the underlying hardware and AI processors.

Additionally, the complexity of designing, developing and deploying AI chipsets is growing proportionally with the emergence of AI models and the increasing number of neural layers and parameters required. The highly competitive chipset landscape has led to developers dealing with heterogeneous and fragmented hardware solutions not necessarily optimized for all AI networks.

Chipset vendors are racing to enhance the performance of their chipset solutions and bring new hardware features to accommodate the latest AI models. However, pushing the boundaries of hardware capabilities isn’t sufficient for tapping into innovation brought by the latest AI models and democratizing the use of AI within the enterprise.

Most AI developers and data scientists are stuck in this new dynamic of learning how to use new networks and, simultaneously, tapping into the hardware capabilities to address new AI use cases and models. They spend significant time building custom programs and code to meet the performance expectations required by these new use cases. Ideally, these developers prefer not to waste time and resources in resolving compatibility issues, integrating or optimizing their code for specific hardware, or testing every new AI technology and hardware available.

At the same time, developers are also struggling with other challenges:

• The market is seeing increasingly heterogeneous hardware implementations where different chipset architectures, such as graphic processing unit (GPU), central processing unit (CPU), field programmable gated array (FPGA) and AI accelerators, are designed to address specific AI functions. Developers have to deal with a very complex task to distribute the workload of their AI models across multiple processing architectures with poor optimization and integration between AI development software and the target chipset.

• The constant need to learn new AI techniques and ways to integrate and optimize them prevents developers from focusing on what they are best at, creating innovative applications without worrying about hardware complexity.

• Complicated and unfamiliar tools often slow down the development process and time-to-market, while increasing the overall cost to the developer.

• Lack of future-proof hardware that enables developers to create innovative applications consistent with their current and future business needs while also accommodating legacy applications.

• Porting AI applications to multiple hardware environments is a complex, time-consuming and expensive process.

• Hardware lock-in, as most AI hardware vendors provide proprietary tools to promote AI application development over their chipsets and systems.

Traditionally, public cloud and pure-play AI software vendors have been the closest partners of data scientists and the developer community, offering them rich toolkits and libraries to augment their experiences and simplify AI model development by minimizing code writing. While these tools lower innovation barriers, they are more general-purpose and not optimized for specific hardware.

Software integration and optimization are where developers need the most help, and this could make a huge difference in promoting AI innovation while lowering the barriers to enterprise-grade AI applications development.

Provided their proximity to hardware, chipset suppliers are best positioned to address the AI software-hardware optimization, integration and execution challenges. These players should now go beyond their remit of creating high-performance AI hardware to accommodate new AI networks. They should address the challenges identified above if they want to remain competitive in the AI market.

Software optimization and integration tools have a lot of benefits for AI developers and data scientists, including:

Improved resource utilization: Enables developers to downplay pipeline workloads and reduce power consumption, bandwidth needs and associated operating costs. All these elements help developers save dollars when using cloud services.

Compatibility: Creating versatile applications across multiple hardware solutions and chipset configurations enables developers to generate scale for their applications.

Experience enhancement: Allows developers to create applications with faster performance and smoother experiences in mind.

Security: Building more secure and more reliable applications by reducing the number of contention points and software vulnerabilities.

Low power consumption: Developing greener applications by optimizing energy consumption.

Vendor lock-ins: Developers can easily test various hardware from different vendors and identify hardware that could provide the best performance for their applications.

The most significant impact of this development is the change in the business model. Rather than focusing on AI chipsets and hardware, chipset companies are transitioning their focus over software as a service (SaaS), productizing the open-source framework.

As a result, the industry is witnessing the demise of the “one-and-done” hardware purchase revenue model—and the birth of subscription-based access to AI software platforms. Under this model, hardware features could be unlocked post-manufacturing and on demand to enable users to take advantage of these features to build differentiated applications.

As AI hardware continues to become more ubiquitous and complex to implement, the benefits introduced through integration and optimization cannot be overlooked. AI chipset vendors are expected to double down on offering and monetizing more software solutions.

More on the monetization of AI software development by chipset suppliers will be covered in a dedicated article coming soon, so watch this space.