To address these challenges, the semiconductor industry is exploring new memory system architectures, including stacking memory directly onto processors to reduce the distance needed to move data and lower the power of high-speed data transfers. Other solutions include shrinking bit cells to accommodate more bits on a chip and introducing true 3D DRAMs to further increase storage capacities. The industry is ramping up research and development to continue the growth of AI and support more advanced use cases.
Key takeaways:
- The rapid innovation in AI processing has created a bottleneck known as the 'AI memory wall', where the demand for faster memory to support larger and more capable AI models is outpacing the current capabilities of memory technologies.
- AI models are growing exponentially in size, with OpenAI's GPT-4 model, released in 2023, consisting of 1.76 trillion parameters, a tenfold increase in just three years.
- Memory is a significant bottleneck for GenAI, as these large models require vast amounts of fast memory for training and operation. Current solutions involve using multiple GPUs to split the model and training data.
- The semiconductor industry is responding to these challenges by exploring new memory system architectures, including stacking memory directly onto processors, shrinking bit cells to accommodate more bits on a chip, and developing true 3D DRAMs to increase storage capacities.