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The Next Computing Revolution
The past few decades have witnessed remarkable advancements in computing performance and efficiency, largely driven by Moore’s Law and supported by scalable commodity hardware and loosely coupled software. This technological architecture has enabled online services to reach billions of people worldwide, granting us access to nearly all of human knowledge. However, the forthcoming computing revolution will require significantly more than what has been achieved thus far. To realize the full potential of AI, we must undergo a transformative leap in capabilities that surpasses the progress made during the internet era. Achieving this will necessitate a reevaluation of the foundational elements that propelled previous transformations and a collective effort to innovate across the entire technology stack.
Shifting Trends in Computing
For decades, the prevailing trend in computing has been the democratization of processing power through scale-out architectures utilizing nearly identical, commodity servers. This uniformity has facilitated flexible workload distribution and efficient resource utilization. However, the demands of generative AI, which relies heavily on predictable mathematical operations performed on massive datasets, are reversing this trend. We are now experiencing a significant shift toward specialized hardware—such as ASICs, GPUs, and tensor processing units (TPUs)—which provide substantial enhancements in performance per dollar and per watt compared to general-purpose CPUs. The rise of these domain-specific computing units, optimized for specific tasks, will be crucial for sustaining rapid advancements in AI.
The Need for Specialized Interconnects
As we transition to specialized systems, the requirement for “all-to-all” communication becomes apparent, necessitating terabit-per-second bandwidth and nanosecond latencies that approach local memory speeds. Current networks, primarily based on commodity Ethernet switches and TCP/IP protocols, are ill-equipped to meet these extreme demands. Consequently, to effectively scale generative AI workloads across extensive clusters of specialized accelerators, we are witnessing the emergence of specialized interconnects, such as ICI for TPUs and NVLink for GPUs. These purpose-built networks prioritize direct memory-to-memory transfers and utilize dedicated hardware to expedite information sharing among processors, effectively circumventing the limitations of traditional networking stacks. This shift towards tightly integrated, compute-centric networking is essential for overcoming communication bottlenecks and efficiently scaling the next generation of AI.
Addressing Memory Bandwidth Challenges
For years, the performance improvements in computation have outpaced the growth in memory bandwidth. While techniques such as caching and stacked SRAM have offered partial solutions, the data-intensive nature of AI exacerbates the issue. The relentless demand to supply increasingly powerful compute units has led to the development of high bandwidth memory (HBM), which integrates DRAM directly onto the processor package to enhance bandwidth and minimize latency. However, HBM also faces inherent limitations: the physical chip perimeter constrains total data flow, and the challenge of transferring massive datasets at terabit speeds introduces significant energy constraints. These challenges underscore the urgent need for higher-bandwidth connectivity and highlight the necessity for breakthroughs in processing and memory architecture. Without these advancements, our powerful computing resources may remain underutilized, waiting for data and severely limiting efficiency and scalability.
The Demand for Compute Density
Today’s sophisticated machine learning (ML) models often depend on meticulously coordinated calculations across tens to hundreds of thousands of identical compute elements, consuming vast amounts of power. This close coupling and fine-grained synchronization at the microsecond level create new demands on the system. Unlike heterogeneous systems, ML computations require homogeneous elements; mixing different generations can bottleneck faster units. Communication pathways must also be pre-planned and highly efficient, as delays in a single element can stall an entire process. The extreme demands for coordination and power are driving the need for unprecedented compute density. Reducing the physical distance between processors is crucial to minimize latency and power consumption, paving the way for a new class of ultra-dense AI systems. This pursuit of extreme density and tightly coordinated computation fundamentally reshapes the optimal design for infrastructure, necessitating a radical rethinking of physical layouts and dynamic power management to prevent performance bottlenecks and maximize efficiency.