Think Different, Train Smarter

Why is starting from scratch the only option?

Recent data reveals a stark disparity in the growth rates of hardware computing capabilities versus memory bandwidth. While hardware FLOPS have surged by a staggering 60,000x over the past two decades, DRAM and interconnect bandwidth have lagged significantly, scaling only by factors of 100x and 30x, respectively. This imbalance is becoming increasingly pronounced in the context of AI model training and deployment, particularly for flagship LLMs, which have seen a growth rate of 410x every two years.

The Implications for AI Model Training and Deployment

The memory wall poses a multifaceted challenge. For one, the memory requirements for training AI models are typically several times larger than the number of parameters due to the need to store intermediate activations. This has led to a situation where the design of state-of-the-art neural network models is implicitly influenced by the DRAM capacity of accelerators. Moreover, the communication bottleneck in moving data between accelerators exacerbates the issue, particularly in distributed-memory parallelism setups.

Breaking Through the Wall: Strategies and Solutions

To navigate this memory wall, a holistic approach is required, encompassing both AI model design and hardware architecture.

1. Efficient LLM Architectures: We need to explore beyond the scaling of existing models like BERT and invest in new architecture research. This is a challenging endeavor, as new architectures must be compared against highly optimized existing models.
2. Data Efficient Training Methods: Current neural networks demand vast amounts of data and iterations for learning. We need methods that mirror the human brain's efficiency in learning from fewer examples.
3. Optimization and Training Methodologies: The current dependency on extensive hyperparameter tuning is a significant hurdle. We need more robust training methods that reduce the trial-and-error aspect of model training.
4. Algorithms for Deployment: With memory often being the bottleneck, we need to find ways to deploy LLMs that may be computationally sub-optimal but save on memory. Techniques like speculative decoding are steps in this direction.
5. AI Hardware Design: The focus on increasing peak computing in hardware design needs to be balanced with improving memory-bound workloads. This could involve rethinking the memory hierarchy and bandwidth in AI accelerators.

Conclusion

The journey ahead in AI development is not just about scaling up; it's about scaling smart. As we push the boundaries of what's possible with AI models, we must also innovate in how we train and deploy these models, and how we design the hardware that powers them. The memory wall presents a formidable challenge, but with collaborative effort and innovative thinking, it is a challenge that we can overcome.

This article is based on public materials and collaborative research. The data used for this study is available online.

https://developer.nvidia.com/blog/getting-started-with-large-language-models-for-enterprise-solutions

https://medium.com/riselab/ai-and-memory-wall-2cb4265cb0b8