Boosting Computing Power Beyond Moore’s Law: Song Han’s Breakthrough

The key insight is about scaling computing performance beyond Moore’s Law. By compressing AI models and optimizing the use of memory, we can make AI more efficient and cost-effective, especially in advanced node scenarios. This means we can run large AI models on small devices, reducing serving costs and enabling continuous learning. Additionally, techniques like quantization and sparse tensor usage can further enhance the efficiency of AI models. This research opens up new possibilities for AI in various applications, from language understanding to image generation. The potential for continuous learning and inference is a game-changer in the field of AI. ๐Ÿš€๐Ÿ”ฅ

Key Takeaways ๐Ÿš€

  • AI models have grown much faster than the GPU, leading to a gap between the supply of AI versus the demand of Computing.
  • Recent years have seen a lot of work on area compression, quantizing neuron Nets to the model size, pruning, and sparcity.
  • The use of quantization in conventional Vision to models is not well preserved for weight activation.
  • The bottleneck of the weight activation is not well preserved for large language models.
  • Token attention is the first because it is GL Avail visible all.
  • The use of sparse tensor and sparse layer optimization can reduce training and enable continuous learning compute.

Efficient Computing Beyond Mooreโ€™s Law ๐Ÿ’ป

Song Han is a researcher focused on efficient computing, specifically working on the problem of how to put AI in a chip. He uses the analogy of an elephant and a refrigerator to explain the challenge of limited space for AI models. The solution is to make the models smaller and shrink the AI workload to a specific area. This is done by changing the algorithm while maintaining accuracy and creating a more efficient model.

The Gap Between Supply and Demand ๐Ÿค

The demand for computing has increased significantly, but the supply of AI models has not kept up. There is a gap between the two, which leads to a shortage of computing power. The cost of running AI models is also high, with some models consuming a lot of electricity. As a result, there is a need to solve the environmental impact and reduce the cost of modern computing.

Area Compression and Quantization ๐Ÿ”

One solution to the problem of limited space is area compression, which involves quantizing neuron Nets to the model size. This has been the focus of research in recent years, with publications on pruning and sparcity. However, the use of quantization in conventional Vision to models is not well preserved for weight activation. The bottleneck of the weight activation is not well preserved for large language models.

Token Attention ๐ŸŽ“

Token attention is the first because it is GL Avail visible all. It is a soft function that makes the attention values sum up to one for all, even if they are not two. The goal is to always have them in the KV cache. There is a need to continuously interact and fix the model to enable nonstop conversations.

Continuous Learning Compute ๐Ÿง 

The use of sparse tensor and sparse layer optimization can reduce training and enable continuous learning compute. This is done by exploiting the spatial only compute on the region and passing the convolution to the locations it belongs to. The workload is drastically reduced, and the model is continuously inferencing. The use of sparse tensor and sparse layer optimization is promising, as it can reduce training and enable continuous learning compute.

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