SK Hynix Architect of AI Infrastructure Dismantles the Memory Wall with a Three Layer Full Stack Strategy for Global Computing Environments
The faster proliferation of artificial intelligence has already begun a paradigm shift in priorities in the world semiconductor business. For years virtual hardware developers have been measuring utilities game likes until only a few years ago when the pressure began to come from more complex game requests and bigger data sets until the limits where shifted away from math calculation and into data management, how fast data can be stored, accessed and copied is the real influence that defines what an AI can do.
As corporate development reports released by SK Hynix indicate the manufacturer is preparing itself as an architect of the era of this new infrastructure. In addition to its prominence in the high bandwidth memory (HBM) sector, the hardware provider is assembling a full suite of high end system memory as well as high capacity solid state systems.
The biggest technological challenge of existing AI accelerators is something called the memory wall. The rate at which data travels to the CPU has not seen the same rate of advancement as the speed at which calculations can be performed. This results in a logistical bottleneck as the processor waits for the data that it needs. AI models have a never ending appetite for data both during training and in live use so overall system efficiency can no longer be measured on processing power alone.
In order for modern processors to fully utilize the capabilities of their processing power the associated memory bandwidth must also scale with capacity and power consumption. High bandwidth memory provides a solution to this problem by stacking numerous dynamic random access memory chips on top of each other in a vertical arrangement. This approach provides a significant amount of throughput within a very small area, enabling large amounts of data to be transferred from memory chips directly adjacent to the processor, thus reducing latency and power consumption in high performance applications.
The first stage of AI development centered around model training that demands huge bandwidth and raw capacity to absorb initial data sets. We are in the middle of a rapid change in the present day industry dynamics a shift toward real world deployment and ongoing inferences. With AI tools embedded in search engines enterprise applications smart appliances and personal computers the shape of memory workloads has changed. Inference needs high response times, and fast processing of long context information over and over again for millions of users.
As a result, demand is moving beyond the narrow world of specialized accelerators into the open data stream and full device set. One product category can no longer serve the complete ladder. Advanced memory is now needed up and down the stack from cloud data centers to portable, local devices. You need this data to be pulled quickly, use less power and stay, over time.
In order to stay covered multiple fragmented demands, SK hynix has engaged in handshaking a full stack product plan, and this file is separated into 3 layers of hardware system. And those each layer is ready to take on some pains of big scope in data center design.
The first tier comprises high bandwidth memory solutions such as the future HBM4 (high bandwidth memory 4) directly linking to the latest accelerators, providing data to the GPUs during intensive training. The second tier involves dedicated system memory, like the SOCAMM2 GDDR7 & DDR5 implementations, to improve general platform performance. Lastly, the third tier is made up of ultra high capacity storage solutions like the enterprise class QLC SSD with 245TB storage capacity, for handling large scale inference engine data access.
With corporate data centers and end points demanding ever more customized environments the industry is trending towards ultra specialized hardware deployments. This trend is promoting a separation in the semiconductor industries between processor centric architectures and memory centric computing architectures. This latter relies much more heavily on the efficiency of data movement and reuse than on overall processor clock speed.
Coordination in development will help HBM AI DRAM, AI NAND (Memory Product Group) and SK hynix change its business focus from a component manufacturer to a strategic technology partner. The next, AI scaling phase will depend on how well we can process store and recycle system wise. The convergence of these cutting edge memory technologies makes the physical cornerstones that will enable that change.
