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Memory Wall Problem Grows With LLMs
- The imbalance between data processing needs for training large language models and the slow data movement between memories and processors has sparked a global search for better solutions.
- The forecasted growth of the GPU market to $190 billion by 2029 highlights the importance of scalable matrix multiplication engines like GPUs for AI training.
- Training large language models (LLMs) involves significant matrix multiplications and demands immense computing resources and memory.
- Energy efficiency concerns arise due to the high computational and memory requirements of LLMs, impacting data centers and overall power consumption.
- New research focuses on architectures, algorithms, and computational approaches to address the challenges posed by LLMs.
- Different memory architectures and technologies, such as NOR flash, are being explored to optimize performance and energy efficiency for various tasks.
- Computation-in-memory approaches, like near-memory architectures and the use of emerging memory technologies, aim to reduce data movement and improve processing efficiency.
- Inference tasks on the edge require balancing performance, bandwidth, and power consumption, especially for portable and wearable devices.
- Despite advancements in hardware, overcoming the memory wall and catering to the growing demands of machine learning models may require innovations in algorithms.
- The quest for better algorithms, in addition to advancements in silicon, is crucial for addressing the challenges posed by large language models.
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