By Phil Trickovic, SVP, Tintri

We are witnessing the end of the Boolean compute era. As we approach 2025, the pace of AI development is accelerating, marking a period of unprecedented application delivery innovation. We will see inference applications and inference function specific silicon use cases take root. These motions will usher in the end of a 60-year cycle of software functions that require faster hardware and vice versa. These advances in our understanding of execution paths will deliver exceptional efficiencies in data management and delivery.  Developments in AI hardware and software stack, specifically targeted at our legacy compute stacks. These new and evolving methodologies will deliver innovations and operational efficiencies unseen in the last 50 years.

Delivery of inference applications will impact the currently accepted compute stack.  Deploying GPU, DPU, FPGA, etc. allows us to optimize inefficient subsystem, security and network processes. These new methodologies will reduce unnecessary clock cycles, legacy management tasks, as well as reduce power consumption over time. The next decade's winners will be those who successfully operationalize these improvements to the compute stack.

Rethinking the Three-Tier Architecture

The three-tier block architecture has effectively supported global IT systems for over three decades. Yet, as we edge closer to fully operational AI, its inefficiencies become glaringly apparent. To harness the full potential of AI, we must rethink and overhaul our platform designs from the ground up.

Thermal Efficiency and Processing

One of the most pressing issues in current system architectures is the waste of power in processor clocks, which often operate in an unnecessary (and artificial) wait state. This inefficiency stems from processors being too fast for the tasks they are performing, consuming excess energy without yielding additional computational benefits. The remedy requires a shift towards function-specific edge devices that integrate servers directly into the processing stack, minimizing wasted resources and optimizing power usage.

Function-Specific Edge Devices

Developing function-specific edge devices (function on silicon) is crucial for optimizing AI operations at scale and to the edge. These devices are tailored to perform specific tasks, reducing the need for general-purpose processing power and allowing for more efficient execution of AI models. They can be integrated closely with localized servers, creating a seamless processing environment that enhances speed and reduces latency.

Portability of Applications

The decentralization of applications and dataset is another pivotal area in the evolution of AI systems architecture. Decoupling applications from centralized locations allows for greater flexibility and scalability. AI modules can be employed to prevent split-brain scenarios, ensuring consistency and reliability across distributed systems. This portability enhances the adaptability of applications, enabling them to move seamlessly across different environments without loss of functionality or performance.

Examining the Processing Stack

To achieve the necessary advancements in AI systems architecture, a comprehensive examination of the entire processing stack is required. This involves reassessing every component and cost factor associated with processing, from hardware and software to energy consumption and data management.

Power Consumption and Cost Components

Reducing compute global power consumption is critical not only for environmental sustainability but also for resource management. Currently accepted architectures require substantial energy resources that many commercial entities cannot afford, akin to needing a "Three Mile Island" level of power to operate their large language models (LLMs), computer vision systems, and robotics. Offloading certain tasks to more power-efficient devices or remote servers can reduce the strain on local resources, optimizing power consumption without compromising performance. Success at the edge where power may not be abundantly available will also demand a more power efficient platform. By intelligently distributing workloads strategically, companies can minimize the need for high-power infrastructure at the edge. Re-evaluating cost components and optimizing resource allocation will be vital in making AI systems viable for widespread commercial use.

Integration of Advanced AI Models

Successfully integrating advanced AI models requires a shift from traditional processing methods to more sophisticated architectures that can accommodate the increased complexity and data processing requirements. This includes leveraging AI-driven insights to optimize workflows, enhance decision-making and drive business growth.

Conclusion

As ‘AI' adoption continues to evolve, so too must our approach to systems architecture. By rethinking traditional three-tier structures and adopting more efficient, function-specific designs, we can unlock the full potential of operational AI.

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ABOUT THE AUTHOR

Phil brings 25 years of high-tech experience to Tintri as the Senior Vice President of Revenue. His combined sales and technology acumen has enabled him to successfully lead field organizations, guide countless enterprise customers through evolving technology landscapes, and deliver game-changing business results. Previously, Phil held sales and executive leadership positions at public and private companies including NetApp, EMC, EDS, and most recently at Diamanti, where he drove triple digit revenue growth across Global 1000 market opportunities.