Summary of Clipformer: Key-value Clipping Of Transformers on Memristive Crossbars For Write Noise Mitigation, by Abhiroop Bhattacharjee et al.
ClipFormer: Key-Value Clipping of Transformers on Memristive Crossbars for Write Noise Mitigation
by Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Emerging Technologies (cs.ET)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Transformers have significantly impacted various real-world applications, from natural language processing to computer vision. However, traditional von-Neumann computing faces memory and bandwidth limitations in accelerating transformers due to their massive model sizes. In-memory Computing (IMC) crossbars based on Non-volatile Memories (NVMs), which perform highly parallelized Matrix-Vector-Multiplications (MVMs) with high energy-efficiencies, have emerged as a promising solution for accelerating transformers. The paper investigates the impact of analog MVM operations in crossbars on transformer inference accuracy, specifically focusing on pre-trained Vision Transformers (ViTs). It proposes ClipFormer, a transformation on the Key (K) and Value (V) matrices during inference to boost non-ideal accuracies of pre-trained ViT models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to use powerful computers called transformers for tasks like image recognition. But these computers are really big and need lots of memory and bandwidth to work properly. Researchers have found a way to make them work better using special computer chips that can do many calculations at the same time. However, there’s still a problem: the calculations get a little mixed up when they’re done on these special chips, which makes it harder for the computers to recognize what they see. The researchers came up with an idea called ClipFormer to help fix this problem and make the computers work better. |
Keywords
* Artificial intelligence * Inference * Natural language processing * Transformer * Vit
